A Novel Similarity-Invariant Line Descriptor and Matching Algorithm for Fast and Robust Global Motion Estimation in Navigation Video Sequences - Yammine, Gilbert; Wige, Eugen; Simmet, Franz; Niederkorn, Dieter; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2014)
We present a new and fast line descriptor and matching algorithm to geometrically register video frames that are deformed by a similarity transformation and contain line segments but with very low texture details, such as navigation maps. Line segments are extracted from the frames, and each line is described with a novel line descriptor that does not depend on pixel intensities. The described lines are then matched together and the matches are input to an outlier removal algorithm in order to estimate the parameters of the transformation describing the global motion between the frames. We propose a method for fast parameter estimation of the transformation using line segments instead of points. Additionally, we apply this algorithm in the testing and error detection context of navigation systems, in which we show how to detect map jump artifacts that could occur during the development of these systems, leading to a jerky and unsmooth motion between the frames. The proposed descriptor and its matching algorithm are shown to be fast enough for online use and very robust against a wide range of translation, rotation, and scale changes. Furthermore, the error detection algorithm allows to detect almost all map jump artifacts while maintaining a very low number of false alarm
Keywords: Automatic quality control; global motion estimation; line descriptor; map registration

A novel similarity-invariant line descriptor for geometric map registration - Yammine, Gilbert; Wige, Eugen; Simmet, Franz; Niederkorn, Dieter; Kaup, André (2013 20th IEEE International Conference on Image Processing, ICIP 2013, 2013)
In this paper we present a new line descriptor that can be used in the process of tracking 2D navigation maps that are low in texture details. The problem of tracking this kind of maps is very important in the development and testing phases of a navigation system. In order to track the motion of the map, which could be a translation, rotation, or scale (also known as similarity transform), line segments are used as features and are matched between two consecutive frames using the proposed descriptor in order to determine the motion between the frames. The descriptor is shown to be very robust against typical translation, rotation, and scale transformations, while requiring an acceptable processing time. © 2013 IEE
Keywords: line descriptor; motion tracking; navigation maps

A Novel Viewport-Adaptive Motion Compensation Technique for Fisheye Video - Regensky, Andy; Herglotz, Christian; Kaup, André (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)
Although fisheye cameras are in high demand in many application areas due to their large fields of view, many image and video signal processing tasks such as motion compensation suffer from the introduced strong radial distortions. A recently proposed projection-based approach takes the fisheye projection into account to improve fisheye motion compensation. However, the approach does not consider the large field of view of fisheye lenses that requires the consideration of different motion planes in 3D space. We propose a novel viewport-adaptive motion compensation technique that applies the motion vectors in different perspective viewports in order to realize these motion planes. Thereby, some pixels are mapped to so-called virtual image planes and require special treatment to obtain reliable mappings between the perspective viewports and the original fisheye image. While the state-of-the-art ultra wide-angle compensation is sufficiently accurate, we propose a virtual image plane compensation that leads to perfect mappings. All in all, we achieve average gains of +2.40 dB in terms of PSNR compared to the state of the art in fisheye motion compensatio
Keywords: fisheye camera; motion estimation; motion compensation; block matching; viewport adaptive

An uncertainty decoding approach to noise- and reverberation-robust speech recognition - Maas, Roland; Thippur, Akshaya; Sehr, Armin; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013)
The generic REMOS (REverberation MOdeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hiddenMarkov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios. © 2013 IEE
Keywords: automatic speech recognition; noise robustness; piecewise linear regression; reverberation robustness; uncertainty decoding

Anwendung von Methoden der Signalverarbeitung zur Simulation partieller Differentialgleichungen - Rabenstein, Rudolf (NTG-Fachbericht "Neue Aspekte der Informations- und Systemtheorie", 1983)
LMS::1449

Anwendung von Methoden der Signalverarbeitung zur Simulation partieller Differentialgleichungen - Rabenstein, Rudolf (NTG - Fachberichte, 1983)
UnivIS-Import:2017-07-10:Pub.1983.tech.IE.LEN.anwend

A Phase Grating Approach to Modeling Surface Diffusion in FDTD Room Acoustics Simulations - Kowalczyk, Konrad; van Walstijn, M.; Murphy, D. (IEEE Transactions on Audio Speech and Language Processing, 2011)
In this paper, a method for modeling diffusive boundaries in finite-difference time-domain (FDTD) room acoustics simulations with the use of impedance filters is presented. The proposed technique is based on the concept of phase grating diffusers, and realized by designing boundary impedance filters from normal-incidence reflection filters with added delay. These added delays, that correspond to the diffuser well depths, are varied across the boundary surface, and implemented using Thiran allpass filters. The proposed method for simulating sound scattering is suitable for modeling high frequency diffusion caused by small variations in surface roughness and, more generally, diffusers characterized by narrow wells with infinitely thin separators. This concept is also applicable to other wave-based modeling techniques. The approach is validated by comparing numerical results for Schroeder diffusers to measured data. In addition, it is proposed that irregular surfaces are modeled by shaping them with Brownian noise, giving good control over the sound scattering properties of the simulated boundary through two parameters, namely the spectral density exponent and the maximum well dept
LMS::938
Keywords: Acoustic refraction; acoustic scattering; acoustic signal processing; architectural acoustics; digital filters; finite-difference time-domain; FDTD; methods; fractals

A Physical String Model with Adjustable Boundary Conditions - Schäfer, Maximilian; Frenstátský, Petr; Rabenstein, Rudolf (19th International Conference on Digital Audio Effects (DAFx-16), 2016)
The vibration of strings in musical instruments depends not only on their geometry and material but also on their fixing at the ends of the string. In physical terms it is described by impedance boundary conditions. This contribution presents a functional transformation model for a vibrating string which is coupled to an external boundary circuit. Delay-free loops in the synthesis algorithm are avoided by a state-space formulation. The value of the boundary impedance can be adjusted without altering the core synthesis algorith

Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks - Körner, Georg; Hoffmann, Marcel; Stief, Patrick; Zhang, Mengyu; Rückert, Rainer; Herglotz, Christian; Kaup, André; Vossiek, Martin (IEEE Journal of Microwaves, 2021)
Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics.
Keywords: Radar; OFDM modulation; OFDM; Radar antennas; Compression algorithms; data compression; source coding; FMCW radar; Radar applications; Process control; Antennas

Application of a double-talk resilient DFT-domain adaptive filter for bin-wise stepsize controls to adaptive beamforming - Herbordt, Wolfgang; Buchner, Herbert; Nakamura, S.; Kellermann, Walter (Proc. Int. Workshop on Nonlinear Signal and Image Processing (NSIP), 2005)
In adaptive filtering, undetected noise bursts often disturb the adaptation and may lead to instabilities and divergence of the adaptive filter. The sensitivity against noise bursts increases with the convergence speed of the adaptive filter and limits the performance of signal processing methods where fast convergence is required. Typical applications which are sensitive against noise bursts are adaptive beamforming for audio signal acquisition or acoustic echo cancellation, where noise bursts are frequent due to undetected double-talk. In this paper, we apply double-talk resistant adaptive filtering (Gaensler (1998)) using a nonlinear optimization criterion to adaptive beamforming in the discrete Fourier transform domain for bin-wise adaptation controls. We show the efficiency of double-talk resilient adaptive filtering for a generalized sidelobe canceller for speech and audio signal acquisition. The improved robustness leads to faster convergence, to higher noise reduction, and to a better output signal quality in turn. © 2005 IEEE<br
LMS::543

Application of block based physical modeling for digital sound synthesis of brass instruments - Petrausch, Stefan; Rabenstein, Rudolf (4th European Congress on Acustics, Forum Acusticum 2005, 2005)
Block based approaches for digital sound synthesis via physical modeling have a number of advantages compared to classical physical modeling methods. They follow a divide and conquer approach, where complex models are split up into several blocks, which are solved separately. The resulting discrete solutions are joint by a suitable interaction topology, assuring the correct physical interaction. In this paper the application of block based modeling for digital sound synthesis of brass instruments is demonstrated. The brass instrument model is split up into four parts, a lip model, a mouthpiece model, an air-column model, and a horn model. These block models are solved independently using even different modeling paradigms. The four discrete solutions are transformed slightly by adding a wave port with methods described in literature. As a final solution a simple wave digital filter network is presented, that includes the block models as network elements and simulates brass instruments in real-tim

Application of block-based physical modelling for digital sound synthesis of string instruments - Petrausch, Stefan; Rabenstein, Rudolf (118th Convention Spring Preprints 2005, 2005)
Block-based physical modeling is a recently introduced method for digital sound synthesis via the simulation of physical models. The method follows a "divide-and-conquer" approach, where the elements are individually modeled and discretized, while their interaction topology is separately implemented. In this paper the application of this approach to string instruments is presented. The string is modeled with the functional transformation method, preserving all varieties of strings by a direct link from the physical string parameters to the algorithm. The excitation of the string is modeled separately with wave digital filters. Thanks to the block based approach it is possible to simulate all kind of string instruments, i.e. piano, guitar, and violin, with the same piece of code for the string

Application of mixed modeling strategies for the simulation of the 2D wave equation for arbitrary geometries - Petrausch, Stefan; Rabenstein, Rudolf (15th European Signal Processing Conference, EUSIPCO 2007, 2007)
For the simulation of multi-dimensional systems, currently so called block based methods are under investigation. A physical model is split into a number of blocks, each corresponding to a specific spatial region, which are modeled and realized separately. The correct interaction of these blocks is guaranteed by interaction laws, which can be derived from the assumption of a global model. In doing so, this paper presents an application of mixed modeling strategies for the 2D wave equation, where different blocks are realized with different methods. On the one hand the Finite Difference Time Domain (FDTD) approach can model arbitrary geometries, but suffers from numerical dispersion. The Functional Transformation Method (FTM) on the other hand, is completely free of numerical dispersion, but is restricted to simple geometries. Via a combination of both methods, it is possible to model arbitrary geometries, while large parts of the modeling region are realized free of dispersion with the FTM. © 2007 EURASI

Application of model reduction techniques to building energy simulation - Rabenstein, Rudolf (Solar Energy, 1994)
The physical description of the energy balance of a building leads to a system of coupled differential or difference equations. It can be written in a unifying form using state space techniques, known from control theory. Depending on the level of detail of the building description and on the chosen physical and mathematical models, the resulting system of equations may be of a high degree. Model reduction techniques attempt to simulate the system performance with a reduced set of equations without deteriorating the quality of the simulation results significantly. This enables a computer simulation with reduced numerical expense. After a short introduction into the theory of model reduction by balanced state space realisation, it will be shown how this method is successfully applied to the energy simulation of buildings.

Application of Multidimensional Wave Digital Filters to Boundary Value Problems - Krauss, Herbert; Rabenstein, Rudolf (IEEE Signal Processing Letters, 1995)
The application of multidimensional wave digital filter principles to the numerical solution of partial differential equations has been introduced in various recent publications. However, the examples presented so far did not explicitly consider boundary conditions. This letter shows how to incorporate boundary values into a multidimensional wave digital filter. A lossy transmission line of finite extent with given terminations is chosen as an example. © 1995 IEEE

Application of Operator Theory for the Representation of Continous and Discrete Distributed Parameter Systems - Dymkou, Vitali (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2006)

Application of Operator Theory to Discrete Simulation of Continuous Systems - Dymkou, Vitali; Rabenstein, Rudolf; Steffen, Peter (Proc. in 16th International Symposium on Mathematical Theory of Networks and Systems(MTNS2004), 2004)
LMS::507

Applications of Information Hiding and Digital Watermarking - Eggers, Joachim; Bäuml, R.; Tzschoppe, R.; Huber, J. (ECDL 2001, Workshop Generalized Documents, 2001)
LMS::214

Approximate Partially Decentralized Linear EZF Precoding for Massive MU-MIMO Systems - Kaziu, Brikena; Shanin, Nikita; Spano, Danilo; Wang, Li; Gerstacker, Wolfgang; Schober, Robert (100th Vehicular Technology Conference (VTC2024-Fall), 2024)

Approximation of enzyme kinetics for high enzyme concentration by a ﬁrst order perturbation approach - Kram, Sebastian; Schäfer, Maximilian; Rabenstein, Rudolf (Journal of Mathematical Chemistry, 2018)
This contribution presents an approximate solution of the enzyme kinetics problem for the case of excess of an enzyme over the substrate. A ﬁrst order perturbation approach is adopted where the perturbation parameter is the relation of the substrate concentration to the total amount of enzyme. As a generalization over existing solutions for the same problem, the presented approximation allows for nonzero initial conditions for the substrate and the enzyme concentrations as well as for nonzero initial complex concentration. Nevertheless, the approximate solution is obtained in analytical form involving only elementary functions like exponentials and logarithms. The presentation discusses all steps of the procedure, starting from amplitude and time scaling for a non-dimensional representation and for the identiﬁcation of the perturbation parameter. Suitable time constants lead to the short term and long term behaviour, also known as the inner and outer solution. Special attention is paid to the matching process by the deﬁnition of a suitable intermediate layer. The results are presented in concise form as a summary of the required calculations. An extended example compares the zero order and ﬁrst order perturbation approximations for the short term and long term solution as well as the uniform solution. A comparison to the numerical solution of the initial set of nonlinear ordinary differential equations demonstrates the achievable accuracy.
Keywords: Enzyme kinetics; Perturbation approach; Nonlinear ordinary differential equations

A Python-based Laboratory Course for Image and Video Signal Processing on Embedded Systems - Jaskolka, Karina; Seiler, Jürgen; Beyer, Frank; Kaup, André (Heliyon, 2019)

Arc Fault Detection in DC Microgrids - Strobl, Christian (2015 IEEE First International Conference on DC Microgrids (ICDCM), 2015)
Compared to conventional AC grids, DC microgrids demand different switchgear and safety concepts. Electric arcs across mechanical contacts of circuit breakers during switching operations and also arc faults within the installation are more stable in the case of continuous current. Model based methods are important tools not only for selecting the appropriate method of converter control, but also for analyzing the boundary conditions for faults and to develop reliable arc fault detection device
LMS::1635
Keywords: arc fault detection; circuit breakers; switchgear; model based methods; large-signal analysis; small-signal analysis

A Real-Time Acoustic Human-Machine Front-End for Multimedia Applications Integrating Robust Adaptive Beamforming and Stereophonic Acoustic Echo Cancellation - Herbordt, Wolfgang; Ying, J.; Buchner, Herbert; Kellermann, Walter (Proc. Int. Conf. on Spoken Language Processing (ICSLP), 2002)
LMS::362

A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments - Aichner, Robert; Buchner, Herbert; Yan, F.; Kellermann, Walter (Signal Processing, 2006)
In this paper, we present an efficient real-time implementation of a broadband algorithm for blind source separation (BSS) of convolutive mixtures. A recently introduced generic BSS framework based on a matrix formulation allows simultaneous exploitation of nonwhiteness and nonstationarity of the source signals using second-order statistics. We demonstrate here that this general scheme leads to highly efficient real-time algorithms based on block-online adaptation suitable for ordinary PC platforms. Moreover, we investigate the problem of incorporating noncausal delays which are necessary with certain geometric constellations. Furthermore, the robustness against diffuse background noise, e.g., in a car environment is examined and a stepsize control is proposed which further enhances the robustness in real-world environments and leads to an improvement in separation performance. The algorithms were investigated in a reverberant office room and in noisy car environments verifying that the proposed method ensures high separation performance in realistic scenarios. © 2005 Elsevier B.V. All rights reserve
Keywords: Blind source separation; Convolutive mixtures; Noisy BSS; Real-time; Second-order statistics; Stepsize control

A real-time demonstrator for the 2D localization of two sound sources using blind adaptive mimo system identification - Lombard, Anthony; Buchner, Herbert; Kellermann, Walter (2008 Hands-free Speech Communication and Microphone Arrays, HSCMA 2008, 2008)
A real-time demonstrator for the 2D localization of two sound sources using two microphone pairs is presented and evaluated. The scheme relies on Blind Source Separation (BSS) to adaptively identify the acoustical MIMO system, hence allowing the estimation of relative time delays for each source and each dimension. Extending our previously presented work [1], a mechanism to solve a pairing problem occuring in the multidimensional localization of several sources is described. It exploits the inherent signal extraction abilities of BSS. Experimental evaluations with large microphone apertures show that the demonstrator can accurately localize two speech sources in a 2D space, with a precision better than one degree. ©2008 IEE
Keywords: Acoustic source localization; Blind source separation; Real-time demonstrator; TDOA estimation

A real-time face tracker for color video - Spors, Sascha; Rabenstein, Rudolf (2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, 2001)
This paper presents a face localization and tracking algorithm which is based upon skin color detection and principle component analysis (PCA) based eye localization. Skin color segmentation is performed using statistical models for human skin color. The skin color segmentation task results in a mask marking the skin color regions in the actual frame, which is further used to compute the position and size of the dominant facial region utilizing a robust statistics-based localization method. To improve the results of skin color segmentation a foreground/background segmentation and an adaptive background update scheme were added. Additionally the derived face position is tracked with an Kalman filter. To overcome the problem of skin color ambiguity an eye detection algorithm based upon the principle component analysis (PCA) is presented.

A Reconstruction Algorithm with Multiple Side Information for Distributed Compression of Sparse Sources - van Luong, Huynh; Seiler, Jürgen; Kaup, André; Forchhammer, Soren (Data Compression Conference (DCC), 2016)
We consider the task of reconstructing target signals which are processed as sparse sources for a distributed compression scenario, where communication between the sources is prohibited, however, correlation of information among sources can be utilized at the decoder. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI) for such distributed sparse sources. The proposed algorithm takes advantage of both a compressive sensing (CS) reconstruction with SI and an iteratively weighted ℓ 1 -norm minimization by solving a general weighted multi-ℓ 1 (or n-ℓ 1 ) minimization. To utilize the known multiple SIs, the algorithm computes optimal weights on not only each individual SI but among SIs where the weights are adaptively updated according to changes at every iteration of the reconstruction. By this optimization, the proposed reconstruction algorithm with multiple SI (RAMSI) can robustly exploit the multiple SIs with different qualities. We experimentally demonstrate our algorithm on compressing feature histograms as sparse sources which are extracted from a multi-view image database for multi-view recognition. The results show that the RAMSI with multiple SIs efficiently outperforms the ℓ 1 minimization and also the CS reconstruction with only one S

Artificial neural network-based feature combination for spatial voice activity detection - Meier, Stefan; Kellermann, Walter (Interspeech, 2016)
For many applications in speech communications and speech-based human-machine interaction, a reliable Voice Activity Detection (VAD) is crucial. Conventional methods for VAD typically differentiate between a target speaker and background noise by exploiting characteristic properties of speech signals. If a target speaker should be distinguished from other speech sources, these conventional concepts are no longer applicable, and other methods, typically exploiting the spatial diversity of the individual sources, are required. Often, it is beneficial to combine several features in order to improve the overall decision. Optimum combinations of features, however, depend strongly on the scenario, especially on the position of the target source, the characteristics of noise and interference and the Signal-to-Interference Ratio (SIR). Moreover, choosing detection thresholds which are robust to changing scenarios is often a difficult problem. In this paper, these issues are addressed by introducing Artificial Neural Networks (ANNs) for spatial voice activity detection, which allow to combine several features with background information. The experimental results show that already small ANNs can significantly and robustly improve the detection rates, offering a valuable tool for VAD.
Keywords: voice activity detection; artificial neural network; generalized cross-correlation; receiver operating characteristic

A Self-Trained Model for Cloud, Shadow and Snow Detection in Sentinel-2 Images of Snow- and Ice-Covered Regions - Nambiar, Kamal Gopikrishnan; Morgenshtern, Veniamin; Hochreuther, Philipp; Seehaus, Thorsten; Braun, Matthias (Remote Sensing, 2022)
Screening clouds, shadows, and snow is a critical pre-processing step in many remote-sensing data processing pipelines that operate on satellite image data from polar and high mountain regions. We observe that the results of the state-of-the-art Fmask algorithm are not very accurate in polar and high mountain regions. Given the unavailability of large, labeled Sentinel-2 training datasets, we present a multi-stage self-training approach that trains a model to perform semantic segmentation on Sentinel-2 L1C images using the noisy Fmask labels for training and a small human-labeled dataset for validation. At each stage of the proposed iterative framework, we use a larger network architecture in comparison to the previous stage and train a new model. The trained model at each stage is then used to generate new training labels for a bigger dataset, which are used for training the model in the next stage. We select the best model during training in each stage by evaluating the multi-class segmentation metric, mean Intersection over Union (mIoU), on the small human-labeled validation dataset. This effectively helps to correct the noisy labels. Our model achieved an overall accuracy of 93% compared to the Fmask 4 and Sen2Cor 2.8, which achieved 75% and 76%, respectively. We believe our approach can also be adapted for other remote-sensing applications for training deep-learning models with imprecise labels.<br
Keywords: deep learning; semi-supervised learning; semantic segmentation; self-training; automatic cloud screening; Fmask; Sentinel-2 imagery

A short review of signals and systems for spatial audio - Annibale, Paolo; Rabenstein, Rudolf; Spors, Sascha; Steffen, P. (17th European Signal Processing Conference, EUSIPCO 2009, 2009)
Processing of one-dimensional signals for all kinds of applications is based on the mature theory of signals and systems. On the other hand, processing methods for acoustical signals have been developed in quite diverse application fields like seismic engineering, sonar, audio engineering, and communication acoustics. Similar techniques and algorithms appear under different names and with different notation. This short course tries to unify some of the general concepts of acoustical signal processing. First, it covers some basics from multidimensional signals and systems. By considering the acoustic wave equation as description of a special multidimensional system, its solutions can be represented in various domains regarding time, space, and their associated frequency domains. Spatial descriptions in various coordinate systems and their corresponding Fourier transforms are discussed. © EURASIP, 200

A Signal-Processing Approach to the Digital Simulation of Multi-Dimensional Continuous Systems - Rabenstein, Rudolf (1986)
LMS::1589

A Signal Processing Approach to the Numerical Solution of Parabolic Differential Equations - Rabenstein, Rudolf (Second European Conference on Multigrid Methods, 1985)
LMS::1588

A Simple Multiple Video Objects Rate Control Algorithm for MPEG-4 Real-Time Applications - Rauschert, R.; Kaup, André (3. ITG Conference on Source and Channel Coding, ITG-Fachbericht 159, 2000)
LMS::462

A Simplified Decoding Method for a Robust Distant-Talking ASR Concept Based on Feature-Domain Dereverberation - Sehr, Armin; Kellermann, Walter (Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2008)
A simplified decoding method for the concept of REverbera-tion MOdeling for Speech recognition (REMOS) [1] is pro-posed. In order to achieve robust distant-talking Automatic Speech Recognition (ASR), the REMOS concept uses a com-bination of clean-speech HMMs and a reverberation model to perform feature-domain dereverberation during decoding. The simplified decoding/dereverberation method proposed in this contribution significantly reduces the computational com-plexity of the concept without a major performance reductio
LMS::764
Keywords: dereverberation; robust ASR; reverberation model; feature-domain processing

A Simplified Design of Multidimensional Transfer Function Models - Petrausch, Stefan; Rabenstein, Rudolf (International Workshop on Spectral Methods and Multirate Signal Processing (SMMSP2004), 2004)
LMS::512

A Single Antenna Interference Cancellation Algorithm for GSM - Meyer, Raimund; Gerstacker, Wolfgang; Schober, Robert; Huber, Johannes (Conference Proceedings, 2005)
In mobile communication networks, system capacity is often limited by cochannel interference. Therefore, receiver algorithms for cancellation of cochannel interference have recently attracted much interest At the mobile terminal, algorithms can usually rely only on one received signal delivered by a single receive antenna. In this paper, a novel low-complexity single antenna interference cancellation (SAIC) algorithm for real-valued modulation formats referred to as mono interference cancellation (MIC) is introduced which is well suited for practical applications. By using this algorithm in the mobile terminals, capacity of GSM networks can be improved by up to 40-60 %. © 2005 IEEE.
UnivIS-Import:2015-04-16:Pub.2005.tech.IE.mobilk.asingl
Keywords: Cochannel interference; Equalization; GSM; Mobile communications

A Single Antenna Interference Cancellation Algorithm for Increased GSM Capacity - Meyer, Raimund; Gerstacker, Wolfgang; Schober, Robert; Huber, Johannes (IEEE Transactions on Wireless Communications, 2006)
In mobile communications networks, system capacity is often limited by cochannel interference. Therefore, receiver algorithms for cancellation of cochannel interference have recently attracted much interest. At the mobile terminal, algorithms can usually rely only on one received signal delivered by a single receive antenna. In this letter, a low-complexity single antenna interference cancellation (SAIC) algorithm for real-valued modulation formats referred to as mono interference cancellation (MIC) is introduced which is well suited for practical applications. Field trials in commercial GSM networks using prototype terminals with the proposed MIC algorithm have demonstrated that the novel concept may yield capacity improvements of up to 80%. The underlying principle is also beneficial for adjacent channel interference and receivers with multiple antennas. Furthermore, in coverage-limited scenarios, there is no performance degradation compared with conventional receivers. © 2006 IEEE.
UnivIS-Import:2015-03-09:Pub.2006.tech.IE.mobilk.asingl
Keywords: Cochannel interference; Equalization; GSM; Mobile communications

A spatial-frequency selective approach for modelling the phenomenon of perceptual 'filling in' in human vision - Kaup, André; Aach, T. (Elsevier Science Publishers, 1993)
LMS::1060

A spectrum-enhanced attention model for semantic segmentation of remote sensing images - Li, Xin; Xu, Feng; Tao, Feifei; Tong, Yao; Lyu, Xin; Zhong, Jianyi; Kaup, André (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2025)
Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.
CRIS-Team Scopus Importer:2025-05-09
Keywords: frequency transformer; remote sensing images; Semantic segmentation; spectrum enhancement

A state-space partitioned-block adaptive filter for echo cancellation using inter-band correlations in the Kalman gain computation - Luis Valero, Maria; Mabande, Edwin; Habets, Emanuël (Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2015)
A partitioned-block-based architecture for a model-based acoustic echo canceller in the frequency domain was recently presented. Partitioned-block-based frequency domain adaptive filters provide a lower algorithmic delay compared to the non-partitioned formulations, which is achieved by partitioning the acoustic echo path and hence shortening the time-frequency transforms. Under these circumstances, avoiding the linearisation of the involved circular convolutions can significantly impair the performance. This paper extends the already proposed diagonalization of the Kalman gain matrix by taking into account the neglected inter-band correlations to improve the performance of the acoustic echo canceller. This comes at the cost of a moderate increase in the algorithmic complexit
Keywords: Frequency Domain Adaptive Filters; Acoustic Echo Cancellation; Kalman Filter

A statistical analysis of the kernel-based MMSE estimator with application to image reconstruction - Peinado, Antonio; Koloda, Jan; Gómez, Ángel; Sánchez, Victoria (Signal Processing-Image Communication, 2017)
In this paper we carry out a statistical analysis of a multivariate minimum mean square error (MMSE) estimator developed from a nonparametric kernel-based probability density function. This kernel-based MMSE (KMMSE) estimation has been recently proposed by the authors and successfully applied to image and video reconstruction. The statistical analysis that we present here allows us to understand the utility and limitations of this estimator. Thus, we propose a couple of estimation error measures intended for locally linear signals and show how KMMSE can approximate a sparse estimator. Also, we study how the estimation error propagates when signals are reconstructed recursively, that is, when already-reconstructed samples are used for estimating new samples. As an application, we focus on the problem of the filling ordering (FO) associated to the reconstruction of heterogeneous image blocks. Thus, borrowing the concept of soft data from missing data theory, the error measures established in the first part of the paper can be transformed into reliability measures from which a novel FO procedure is developed. We show that the poroposed FO outperforms other state-of-the-art procedure
Keywords: Recursive signal reconstruction; Multivariate kernel-based regression and estimation; Sparse linear prediction; Minimum mean square error estimation; Image/video error concealment; Filling ordering

A Statistical Framework for Change Detection in Image Sequences - Aach, T.; Kaup, André; Mester, R. (13ieme Colloque sur le Traitement du Signal et des Images, Juan-Les-Pins, 1991)
LMS::1007

A stereophonic acoustic front-end for distant-talking interfaces based on blind source separation - Reindl, Klaus; Maas, Roland; Schwarz, Andreas; Zheng, Yuanhang; Meier, Stefan; Sehr, Armin; Kellermann, Walter (Deutsche Jahrestagung für Akustik (DAGA), 2012)
In this contribution, an acoustic front-end for distanttalking interfaces that only requires two microphone signals is presented. It comprises a directional blind source separation(BSS)-based noise and interference estimation scheme and Wiener-type filters for noise and interference suppression. The proposed front-end and its integration into a speech recognition system is analyzed and evaluated in noisy living-room-like environments representing a generalization of the PASCAL CHiME challenge, implying that the potential of the method is verified for a wide range of distance

A stereophonic acoustic signal extraction scheme for noisy and reverberant environments - Reindl, Klaus; Zheng, Yuanhang; Schwarz, Andreas; Meier, Stefan; Maas, Roland; Sehr, Armin; Kellermann, Walter (Computer Speech and Language, 2012)
In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation ('directional BSS'), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions. © 2012 Elsevier Ltd. All rights reserve
UnivIS-Import:2015-03-09:Pub.2012.tech.IE.LEN.astere_9
Keywords: Blind source extraction; PASCAL CHiME challenge; Robust automatic speech recognition; Speech enhancement

A Stochastic Model for Image Segmentation Involving Constrained Least Squares Estimation - Kaup, André; Aach, T. (IEEE International Symposium on Information Theory, Trondheim, 1994)
LMS::1025

A String in a Room: Mixed-Dimensional Transfer Function Models for Sound Synthesis - Schäfer, Maximilian; Schlecht, Sebastian J.; Rabenstein, Rudolf (23rd International Conference on Digital Audio Effects (DAFx-20), 2020)
Physical accuracy of virtual acoustics receives increasing attention due to renewed interest in virtual and augmented reality applications. So far, the modeling of vibrating objects as point sources is a common simplification which neglects effects caused by their spatial extent. In this contribution, we propose a technique for the interconnection of a distributed source to a room model, based on a modal representation of source and room. In particular, we derive a connection matrix that describes the coupling between the modes of the source and the room modes in an analytical form. Therefore, we consider the example of a string that is oscillating in a room. Both, room and string rely on well established physical descriptions that are modeled in terms of transfer functions. The derived connection of string and room defines the coupling between the characteristic string and room modes. The proposed structure is analyzed by numerical evaluations and sound examples on the supplementary websit

A STUDY ON THE EFFECT OF COLOR SPACES IN LEARNED IMAGE COMPRESSION - Prativadibhayankaram, Srivatsa; Panda, Mahadev Prasad; Seiler, Jürgen; Richter, Thomas; Sparenberg, Heiko; Foessel, Siegfried; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2024)
In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work, which consists of two branches-one for the luminance component (Y or L) and another for chrominance components (UV or AB). However, for the RGB variant we input all 3 channels in a single branch, similar to most learned image codecs operating in RGB. The models are trained for multiple bitrate configurations in each color space. We report the findings from our experiments by evaluating them on various datasets and compare the results to state-of-the-art image codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM with a Bjøntegaard delta bitrate (BD-BR) gain of 7.5% using VTM intra-coding mode as the baseline. Whereas the LAB variant has a better performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8%. Overall, the RGB variant of SLIC achieves the best performance with a BD-BR gain of 13.14% in terms of MS-SSIM and a gain of 17.96% in CIEDE2000 at the cost of a higher model complexity.
CRIS-Team Scopus Importer:2025-04-01
Keywords: color learning; color spaces; Deep learning; image compression; learned image compression; variational autoencoder

A summary of the REVERB challenge: State-of-the-art and remaining challenges in reverberant speech processing research - Kinoshita, Keisuke; Delcroix, Marc; Gannot, Sharon; Habets, Emanuël; Haeb-Umbach, Reinhold; Kellermann, Walter; Leutnant, Volker; Maas, Roland; Nakatani, Tomohiro; Raj, Bhiksha; Sehr, Armin; Yoshioka, Takuya (EURASIP Journal on Advances in Signal Processing, 2016)
In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processin
Keywords: Automatic speech recognition; Dereverberation; Evaluation campaign; REVERB challenge; Reverberation

Asymmetric Watermarking Schemes - Eggers, Joachim; Su, Jonathan; Girod, Bernd (Informatik aktuell, 2000)
LMS::199

A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation - Haubner, Thomas; Halimeh, Mhd Modar; Brendel, Andreas; Kellermann, Walter (European Signal Processing Conference (EUSIPCO), 2021)

A System for WFS Rendering of Multiple Moving Sound Sources from Manual Input - Ammann, M.; Brendel, J.; Kuntz, Achim; Rabenstein, Rudolf (1st DEGA Symposium: Workshop Wave Field Synthesis, 2007)
LMS::743

A time-domain implementation of data-independent robust broadband beamformers with lowfilter order - Mabande, Edwin; Schad, Adrian; Kellermann, Walter (2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays, HSCMA'11, 2011)
Broadband data-independent beamforming designs aiming at constant beamwidth lead to superdirective beamformers for low frequencies, if the array size is small relative to the wavelengths. Constraints on delay and complexity often render beamformers with low filter order desirable. Direct control of the robustness of these beamformers is required if they are to be applied successfully in practice. Here, we present a method where beamformers with low filter order are obtained directly from solving a constrained optimization problem which guarantees a distortionless response in the desired look direction and meets the user-defined robustness constraints. The effectiveness of this method in controlling the robustness and providing good spatial selectivity is shown by design examples. © 2011 IEE
Keywords: Constrained optimization; Robust Broadband Beamformer; White Noise Gain

A Transfer Function Model for 1D Diffusion Processes - Schäfer, Maximilian; Rabenstein, Rudolf (10th International Workshop on Multidimensional (nD) Systems (nDS 2017), 2017)
The dynamics of linear diffusion processes can be modelled by multi-dimensional transfer functions in a spatiotemporal frequency domain. Finite spatial domains with simple and impedance type boundary conditions are possible. The resulting discrete time synthesis algorithm captures the essential behaviour with a restricted number of modes. Dynamic diffusion processes are of interest for molecular communication as shown by several simulation example

A transformation domain description for nonlinear partial differential equations - Rabenstein, Rudolf; Trautmann, Lutz (2000 10th European Signal Processing Conference, EUSIPCO 2000, 2000)
Transfer function models for the description of physical systems with distributed parameters have recently been introduced to the field of multidimensional digital signal processing. They are an extension of the transfer functions description for one-dimensional systems and provide an alternative to the conventional representation by partial differential equations. Transfer function models are the starting point for the development of efficient discrete algorithms that are suitable for computer implementation. This paper extends the presented transformation domain description to nonlinear systems. A suitable choice of the integral transformations for the independent variables allows to treat certain types of nonlinearities in a similar way as for linear multidimensional systems.

A Triangulation-Based Backward Adaptive Motion Field Subsampling Scheme - Brand, Fabian; Seiler, Jürgen; Alshina, Elena; Kaup, André (IEEE 22nd Workshop on Multimedia Signal Processing (MMSP), 2020)

Attacks and Benchmarking - Voloshynovskiy, S.; Pereira, S.; Pun, T.; Su, Jonathan; Eggers, Joachim (IEEE Communications Magazine, 2001)
LMS::212

A tutorial on the representation of two-dimensional wave fields by multidimensional signals - Rabenstein, Rudolf; Steffen, Peter; Spors, Sascha (4th International Workshop on Multidimensional Systems, NDS 2005, 2005)
The representation of information by waves is common to many fields in engineering and physics like electromagnetism, acoustics, and optics. Many different tools for the description of wave-like signals have been developed. Most of them are based on methods and results from mathematical physics with different viewpoints according to the specific application areas. This tutorial presents a framework for some of the most common of these representations. It is based on well-known transform domain signal descriptions from multidimensional systems theor

A Two-Channel Acoustic Front-End for Robust Automatic Speech Recognition in Noisy and Reverberant Environments - Maas, Roland; Schwarz, Andreas; Zheng, Yuanhang; Reindl, Klaus; Meier, Stefan; Sehr, Armin; Kellermann, Walter (International Workshop on Machine Listening in Multisource Environments (CHiME), 2011)
An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is pro-posed in this contribution. It comprises a blind source separation-based signal extraction scheme and only re-quires two microphone signals. The proposed front-end and its integration into the recognition system is analyzed and evaluated in noisy living room-like environments ac-cording to the PASCAL CHiME challenge. The results show that the introduced system significantly improves the recognition performance compared to the challenge baselin

A two-channel reverberation suppression scheme based on blind signal separation and Wiener filtering - Schwarz, Andreas; Reindl, Klaus; Kellermann, Walter (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012)
In this paper, we apply a blind signal extraction scheme for two microphones to the problem of dereverberation. The system consists of a blocking matrix that cancels the target signal as well as reverberated components up to a certain time lag, thus obtaining a reference not only for noise and interference, but also for late reverberation, which can then be suppressed with a Wiener filter, while leaving early reverberation components largely intact. The performance is assessed in terms of recognition rate of an automatic speech recognizer trained on clean speech, using sentences from the GRID corpus convolved with measured room impulse responses. We show that the system, although primarily developed for noise and interference suppression in low SNR conditions, can significantly suppress reverberation and thereby improve recognition results. © 2012 IEE
Keywords: blind source separation; Dereverberation; source extraction; speech enhancement

Audio-motor integration for robot audition - Deleforge, Antoine; Schmidt, Alexander; Kellermann, Walter (Multimodal Behavior Analysis in the Wild, 2018)

Audio Signal Processing in the 21st Century: The important outcomes of the past 25 years - Richard, Gaël; Smaragdis, Paris; Naylor, Patrick A.; Makino, Shoji; Gannot, Sharon; Kellermann, Walter; Sugiyama, Akihiko (IEEE Signal Processing Magazine, 2023)
Audio signal processing has passed many landmarks in its development as a research topic. Many are well known, such as the development of the phonograph in the second half of the 19th century and technology associated with digital telephony that burgeoned in the late 20th century and is still a hot topic in multiple guises. Interestingly, the development of audio technology has been fueled not only by advancements in the capabilities of technology but also by high consumer expectations and customer engagement. From surround sound movie theaters to the latest in-ear devices, people love sound and soon build new audio technology into their daily lives as an essential and expected feature.
Keywords: Spatial audio; Signal processing; Telephony; Motion pictures; Audio systems; Acoustic signal processing; History

Audiovisual Congruence and Localization Performance in Virtual Reality: 3D Loudspeaker Model vs. Human Avatar - Hofmann, Anja; Meyer-Kahlen, Nils; Schlecht, Sebastian; Lokki, Tapio (Journal of the Audio Engineering Society, 2024)
This paper investigates audiovisual congruence in virtual reality with both horizontal and vertical offsets between audio and visual rendering. Audiovisual congruence and localization errors are assessed using loudspeaker playback and nonindividualized headphone rendering. To account for the influence of different types of visual information on congruence, presentations of a loudspeaker model and 3D human avatar were compared. Therefore, a new dataset of audiovisual speech was recorded. Results show that human avatar rendering increases perceived congruence, and experienced listeners have an increased tendency to respond with "incongruent" when a loudspeaker model is shown but not when the human avatar is presented. Moreover, a correlation is found between localization precision and audiovisual congruence for horizontally offset stimuli and avatar presentation. For vertical offsets, the angular range of congruence is generally large, and localization errors are high, so no correlation can be observed between the two. The paper contributes congruence ranges for audiovisual speech in virtual reality, which also has implications for augmented reality telepresence use.
Created from Fastlane, Scopus look-up

Auflösungserhöhung durch nichtregelmäßige Abtastung - Schöberl, Michael; Seiler, Jürgen; Kaup, André; Nowak, Arne (Fachzeitschrift für Fernsehen, Film und elektronische Medien, 2012)
Bisher ging die Erhöhung der Auflösung einer Kamera mit der Steigerung der Anzahl der Pixel des Bilsensors einher. Kameras und Aufnahmesysteme sind jedoch im Datendurchsatz, das heißt, wieviele Pixel pro Sekunde aufgenommen und gespeichert werden können, aufgrund der möglichen Leistungsaufnahme und Komplexität der Hardware begrenzt. Es wird daher ein Ansatz vorgeschlagen, bei dem für ein Bild nur relativ wenig Pixel aufgenommen werden und das hochauflösende Bild in einem anschließenden Rekonstruktionsprozess erzeugt wird. Das dafür erforderliche nichtregelmäßige Abtastraster ist dabei so optimiert, dass dennoch Bildsensoren mit regelmäßig angeordneten Ausleseschaltungen eingesetzt werden können. Dadurch wird es möglich, eine existierende Kameraplattform mit nur wenigen Änderungen zu nutzen. Die Bildrekonstruktion erfolgt mit einem Ansatz basierend auf dünn besetzten Repräsentationen. Das Ergebnis ist ein hochaufgelöstes Bild, das mit deutlich geringerem Aufwand bei der Aufnahme erzielt werden kan

A unified framework for multiple arrays on a robot and application to sound localization - Madmoni, Lior; Barfuß, Hendrik; Rafaely, Boaz; Kellermann, Walter (Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA), 2017)
The auditory system of humanoid robots has recently gained significant attention in the research community. This system enables interaction with the surroundings via microphone arrays embodied into the robot. Typically, a single array topology is designed which may limit the performance of the auditory system. In a recent paper, a system with two array types has been studied: the first consists of sensors distributed over the robot’s head, and the second is a body-mounted robomorphic array. These arrays have only been studied separately without exploiting their performance as a single array. In this work, a unified framework has been developed for the two arrays in the spherical harmonics domain, and an initial investigation of direction of arrival estimation (DOA) with the unified framework is presented. The simulation study shows that the unified framework outperforms DOA estimation with the individual arrays, mainly at the low frequency rang
Keywords: Humanoid Robots; Robot Audition; Spherical Harmonics; DOA Estimation

A Unified Probabilistic View on Spatially Informed Source Separation and Extraction Based on Independent Component Analysis - Brendel, Andreas; Haubner, Thomas; Kellermann, Walter (IEEE Transactions on Signal Processing, 2020)

A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis - Brendel, Andreas; Haubner, Thomas; Kellermann, Walter (IEEE Transactions on Signal Processing, 2023)
In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.
CRIS-Team Scopus Importer:2023-04-14
Keywords: Blind source separation; convolutive mixtures; independent component analysis; indpendent vector analysis; trinicon

Aurealization - Rabenstein, Rudolf; Trautmann, Lutz (Principles of 3D Image Analysis and Synthesis, 2000)

AUTO GMM-SAMT: An automatic object tracking system for video surveillance in traffic scenarios - Quast, Katharina; Kaup, André (Eurasip Journal on Image and Video Processing, 2011)
A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations. Copyright © 2011 Katharina Quast and Andr Kau

Automated Automotive Radar Calibration With Intelligent Vehicles - Tsaregorodtsev, Alexander; Buchholz, Michael; Belagiannis, Vasileios (European Signal Processing Conference, 2023)
While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.
CRIS-Team Scopus Importer:2023-12-15
Keywords: automatic calibration; automotive radars; roadside infrastructure

Automatic Detection of Video Display Errors in Car Navigation Systems - Yammine, Gilbert (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2015)
Quality assurance is a crucial step before introducing new systems to the market. In order to assure a high user satisfaction and a good company reputation, system errors are to be avoided in the products. Therefore, the systems have to be thoroughly monitored under different conditions to assure normal operation. In this thesis, a monitoring system for the automatic detection of display errors in a car navigation system is presented. The video signal is captured and each frame is searched for possibly occurring errors. If an error is detected, it is logged for further investigation. The main module of the system consists of error detection plugins, each of which is specialized in detecting one display error. New algorithms for the detection of different types of errors, such as missing symbols and erroneous motion of the map are proposed and tested. In the last part of the thesis, the complete system is described and tested, and is shown to be a practical solution that can be applied in real-time system test

Automatic Heart Localization from 4D MRI Datasets - Sörgel, Wolfgang; Vaerman, V. (SPIE Medical Imaging, 1997)
LMS::389

Automatic Object Detection and Tracking for Video-Based Traffic Surveillance - Quast, Katharina (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2012)
Road and air traffic develops and increases rapidly. To manage the increasing traffic volume on streets and airport aprons, systems for video-based automatic traffic control are needed. Such systems require automatic detection and tracking of moving objects of interest. Since both tasks have to deal with similar challenges synergy effects can be exploited by detecting and tracking objects simultaneously. In this dissertation, an overview over the tasks and challenges of automatic object detection and object tracking for video surveillance of traffic scenarios is given and several object detection and tracking algorithms are introduced and discussed. It is shown that using the proposed methods not only the tracking of the object position, but also a tracking of the object's size and rotation, respectively a tracking of the object contour is possible. Furthermore, a detection and a tracking method are combined in such a way that the detection method serves as an initialization for the tracking method. In this way, a system for automatic object detection and object tracking for video surveillance of traffic scenarios is achieve

Automatic Reconstruction of Stationary 3-D Objects from Multiple Uncalibrated Camera Views - Eisert, Peter; Steinbach, Eckehard; Girod, Bernd (IEEE Transactions on Circuits and Systems For Video Technology, 1999)
LMS::315

Automatic TV logo removal using statistical based logo detection and frequency selective inpainting - Meisinger, Katrin; Tröger, Tobias; Zeller, Marcus; Kaup, André (13th European Signal Processing Conference, EUSIPCO 2005, 2005)
This paper outlines a method for automatically removing logos characterizing a broadcast station in TV sequences. First, the logo is detected automatically based on change detection of moving videos assuming that the image content is changing over time except for the location of the logo. In order to obtain initial logo masks, difference images between frames are binarized by thresholding. The final logo mask is obtained by subsequently refining the change masks by contour relaxation based on Markov Random Fields. Then, the image signal surrounding the logo is extrapolated using a frequency selective method and placed instead of the logo. The proposed algorithm is developed for TV sequences sampled from analog television, dealing thus with real world problems as noise, sampling and real logo

Automatic video structuring for multimedia messaging - Treetasanatavorn, S.; Rauschenbach, U.; Heuer, J.; Kaup, André (11th European Signal Processing Conference, EUSIPCO 2002, 2002)
This paper proposes a low-complexity technique to structure video sequences and generate corresponding meta data supporting adaptive presentations for arbitrary communication terminals. The algorithm structures a video sequence into segments of coherent camera motion. The structured video is presented by representative key-frames, which are characterized by background and moving or stationary objects. The realized prototype incorporates algorithms for camera motion estimation, spatial-temporal segmentation, object tracking, key-frame selection, and meta data (MPEG-7) generation.

Automatische TV-Logo-Entfernung durch statistische modellbasierte Detektion und frequenzselektive Kompensation - Meisinger, Katrin; Tröger, Tobias; Zeller, Marcus; Kaup, André (Elektronische Medien: 11. Dortmunder Fernsehseminar, ITG-Fachbericht 188, 2005)
Bei der TV-Ausstrahlung identifizieren sich Rundfunkanstalten durch Senderlogos. In diesem Beitrag wird eine Methode zur automatischen Entfernung von Senderlogos vorgestellt. Zuerst wird das Logo basierend auf Änderungsdetektion in Videos automatisch detektiert unter der Annahme, dass sich der Bildinhalt mit der Zeit überall außer an der Logostelle ändert. Um Start-Logo-Masken zu erhalten, werden Differenzbilder durch Schwellwertbildung erzeugt. Die finalen Logo-Masken werden danach durch Verfeinerung der Änderungsmasken mit Hilfe von Kantenglättung basierend auf Markov-Zufallsfeldern generiert. Dann wird das das Logo umgebende Bildsignal durch eine frequenzselektive Methode extrapoliert und anstelle des Logos platziert. Der vorgestellte Algorithmus wurde für aus dem deutschen analogen Fernsehen aufgenommene Fernsehbeiträge entwickelt, so dass Probleme die durch Rauschen, Abtastung und tatsächlich verwendeten Logos entstehen, gelöst werden
LMS::569

A Video Codec Incorporating Block-Based Multi-Hypothesis Motion-Compensated Prediction - Flierl, Markus; Wiegand, Thomas; Girod, Bernd (Proc. SPIE Conference on Visual Communications and Image Processing, 2000)
LMS::172

A Wave Digital Kalman Filter Approach for Fault Detection in DC Grids: A Case Study - Rabenstein, Rudolf; Schäfer, Maximilian; Bauer, Johannes; Strobl, Christian (IEEE Int. Symp. on Circuits and Systems (ISCAS), 2018)
Fault detection in DC grids requires new concepts for the design of protection devices. A promising concept for predictive circuit protection is model based learning, e.g. Kalman estimation. It requires detailed system models of electrical circuits. An established modeling technique in circuit theory is the wave digital principle which preserves circuit passivity and stability. This contribution combines both ideas and derives a Kalman filter in the wave digital domain.

A wave-domain model for acoustic MIMO systems with reduced complexity - Schneider, Martin; Kellermann, Walter (2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays, HSCMA'11, 2011)
The identification of loudspeaker enclosure microphone (LEM) systems with large numbers of microphones and loudspeakers is a challenging task for both, algorithmic and computational reasons. Wavedomain adaptive filtering offers an alternative to point-to-point channel identification where the complexity of the LEM system model can be reduced by using a physically motivated approximation. In this paper we present for the first time an explicit discrete-time and discrete-frequency formulation of the necessary transforms to the wave domain for circular loudspeaker and microphone arrays in a practically relevant setup. We further propose a generalization of the LEM system model in the wave domain allowing for a scalable complexity and a flexible trade-off between low complexity and high accuracy. An experimental evaluation of an acoustic echo cancellation scenario supports the applicability of the presented model. © 2011 IEE

Bayesian Algorithms for Adaptive Change Detection in Image Sequences Using Markov Random Fields - Aach, T.; Kaup, André (Signal Processing-Image Communication, 1995)
LMS::1051

Bayesian method for motion segmentation and tracking in compressed videos - Treetasanatavorn, Siripong; Rauschenbach, U.; Heuer, J.; Kaup, André (Computer Science, 2005)
This contribution presents a statistical method for segmentation and tracking of moving regions from the compressed videos. This technique is particularly efficient to analyse and track motion segments from the compression-oriented motion fields by using the Bayesian estimation framework. For each motion field, the algorithm initialises a partition that is subject to comparisons and associations with its tracking counterpart. Due to potential hypothesis incompatibility, the algorithm applies a conflict resolution technique to ensure that the partition inherits relevant characteristics from both hypotheses as far as possible. Each tracked region is further classified as a background or a foreground object based on an approximation of the logical mass, momentum, and impulse. The experiment has demonstrated promising results based on standard test sequences. © Springer-Verlag Berlin Heidelberg 200

Bayesian Model Selection for Nonlinear Acoustic Echo Cancellation - Halimeh, Mhd Modar; Brendel, Andreas; Kellermann, Walter (27th European Signal Processing Conference (EUSIPCO), 2019)

Beamforming for Speech and Audio Signals - Kellermann, Walter (Handbook of Signal Processing in Acoustics, 2008)
If microphone arrays instead of a single microphone are employed for sampling acoustic wavefields, signal processing of the sensor data can exploit the spatial diversity to better detect or extract desired source signals and to suppress unwanted interference. Beamforming represents a class of such multichannel signal processing algorithms and suggests a spatial filtering which points a beam of increased sensitivity to desired source locations while suppressing signals originating from all other locations. While beamforming techniques are also extensively used in other areas, e.g. in underwater acoustics, ultrasound diagnostics, and radio communications [1–3], the treatment is concentrating here on wideband acoustic signals in the audio frequency rang

Beam-Specific System Identification - Halimeh, Mhd Modar; Hofmann, Christian; Kellermann, Walter (ITG Conference on Speech Communication, 2021)

Benchmarking Super-Resolution Algorithms on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2017)
Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variation

Best-basis analysis of broadband tremor signals - Bartosch, Thorsten; Steffen, Peter (Annals of Geophysics, 1999)
LMS::688

BeTrIS - An Index System for MPEG-7 Streams - Kofler-Vogt, A.; Kosch, H.; Heuer, J.; Kaup, André (EURASIP J APPL SIG P, 2006)
The ISO/IEC Motion Picture Group (MPEG) issued in 2002 a Standard, called MPEG-7, which enables the content description of multimedia data in XML. The standard supports applications to exchange, identify, and filter multimedia contents based on MPEG-7 descriptions. However, especially mobile applications that deal with MPEG-7 suffer from limited bandwidth, low computational power, and limited battery life. In this document, we describe an index system adopted from database systems that allows filter mechanisms and random access to encoded MPEG-7 streams and which overcome the limitation of the network and the consuming terminal. Encoding is applied in order to reduce the data rate of the XML documents to be transmitted. The indexed parts of the encoded streams can be accessed without the need to deserialize the complete stream. Furthermore, the system is evaluated and results of the experimental evaluation are discussed. Copyright © 2006 Hindawi Publishing Corporation. All rights reserve

Beyond Bjontegaard: Limits of Video Compression Performance Comparisons - Herglotz, Christian; Kränzler, Matthias; Mons, Ruben; Kaup, André (IEEE International Conference on Image Processing, 2022)
For 20 years, the gold standard to evaluate the performance of video codecs is to calculate average differences between rate distortion curves, also called the "Bjøntegaard Delta". With the help of this tool, the compression performance of codecs can be compared. In the past years, we could observe that the calculus was also deployed for other metrics than bitrate and distortion in terms of peak signal-to-noise ratio, for example other quality metrics such as video multi-method assessment fusion or hardware-dependent metrics such as the decoding energy. However, it is unclear whether the Bjøntegaard Delta is a valid way to evaluate these metrics. To this end, this paper reviews several interpolation methods and evaluates their accuracy using different performance metrics. As a result, we propose to use a novel approach based on Akima interpolation, which returns the most accurate results for a large variety of performance metrics. The approximation accuracy of this new method is determined to be below a bound of 1.5%
Keywords: video codec; rate-distortion; Bjøntegaard Delta; VMAF; decoding energy

Bidirectionally Decodable Streams of Prefix Code Words - Girod, Bernd (IEEE Communications Letters, 1998)
LMS::348

Binaural coherent-to-diffuse-ratio estimation for dereverberation using an ITD model - Zheng, Chengshi; Schwarz, Andreas; Kellermann, Walter; Li, Xiaodong (European Signal Processing Conference (EUSIPCO), 2015)
Most previously proposed dual-channel coherent-to-diffuse-ratio (CDR) estimators are based on a free-field model. When used for binaural signals, e.g., for dereverberation in binaural hearing aids, their performance may degrade due to the influence of the head, even when the direction-of-arrival of the desired speaker is exactly known. In this paper, the head shadowing effect is taken into account for CDR estimation by using a simplified model for the frequency-dependent interaural time difference and a model for the binaural coherence of the diffuse noise field. Evaluation of CDR-based dereverberation with measured binaural impulse responses indicates that the proposed binaural CDR estimators can improve PESQ score
Keywords: Binaural speech dereverberation; coherent-to-diffuse-ratio; interau-ral time difference

Bit Allocation Methods for Closed-Loop Coding of Oversampled Pyramid Decompositions - Horn, Uwe; Wiegand, Thomas; Girod, Bernd (Proc. IEEE International Conference on Image Processing (ICIP 97), 1997)
LMS::423

Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model - Jia, Panqi; Koyuncu, A. Burakhan; Mao, Jue; Cui, Ze; Ma, Yi; Guo, Tiansheng; Solovyev, Timofey; Karabutov, Alexander; Zhao, Yin; Wang, Jing; Alshina, Elena; Kaup, André (2024 Picture Coding Symposium, PCS 2024 - Proceedings, 2024)
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit represen-tations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.
CRIS-Team Scopus Importer:2024-07-19
Keywords: algorithm optimization; bit rate matching; JPEG-AI; Learned Image Compression

Blind estimation of a feature-domain reverberation model in non-diffuse environments with variance adjustment - Wen, Jimi Y C; Sehr, Armin; Naylor, Patrick A.; Kellermann, Walter (17th European Signal Processing Conference, EUSIPCO 2009, 2009)
Blind estimation of a two-slope feature-domain reverberation model is proposed. The reverberation model is suitable for robust distant-talking automatic speech recognition approaches which use a convolution in the feature domain to characterize the reverberant feature vector sequence, e.g. [1, 2, 3]. Since the model describes the reverberation by a matrix-valued IID Gaussian random process, its statistical properties are completely captured by its mean and variance matrices. The suggested solution for the estimation of the model includes two novel features based on the study of simulated rooms: 1) a solution for blindly determining a two-slope decay model from a single-slope estimate; 2) a variance mask to improve the estimation of the variance matrix. Using the proposed solution, the reverberation model can be estimated during recognition without the need of pre-training or using calibration utterances with known transcription. Connected digit recognition experiments using [3] show that the reverberation models estimated by the proposed approach significantly outperform HMM-based recognizers trained on reverberant data in most environments. © EURASIP, 200

Blind frame freeze detection in coded videos - Yammine, Gilbert; Wige, Eugen; Simmet, F.; Niederkorn, D.; Kaup, André (29th Picture Coding Symposium, PCS 2012, 2012)
In multimedia systems, system errors and artifacts should be avoided in order to keep the Quality of Experience (QoE) of the user as high as possible. For that, the system should be tested and monitored for a long time to assure normal operation. In this paper, we present a new algorithm that automatically detects freezing artifacts in coded videos. The system output is captured and encoded, and the error/artifact detection is run later on the decoded video. One major constraint on our detection algorithm is that every freeze should be detected in order to analyze the reason of its occurrence. Such a constraint entails a high number of false alarms when using the standard freeze detection algorithms. For that, we present a new algorithm that keeps the number of true positives to its maximum, while minimizing the number of false positives. © 2012 IEE

Blind GOP structure analysis of MPEG-2 and H.264/AVC decoded video - Yammine, Gilbert; Wige, Eugen; Kaup, André (28th Picture Coding Symposium, PCS 2010, 2010)
In this paper, we provide a simple method for analyzing the GOP structure of an MPEG-2 or H.264/AVC decoded video without having access to the bitstream. Noise estimation is applied on the decoded frames and the variance of the noise in the different I-, P-, and B-frames is measured. After the encoding process, the noise variance in the video sequence shows a periodic pattern, which helps in the extraction of the GOP period, as well as the type of frames. This algorithm can be used along with other algorithms to blindly analyze the encoding history of a video sequence. The method has been tested on several MPEG-2 DVB and DVD streams, as well as on H.264/AVC encoded sequences, and shows successful results in both cases. © 2010 IEE
Keywords: Blind analysis; Gop structure; No-reference metric; Noise estimation; Video forensics

Blind separation of incoherent and spatially disjoint sound sources - Dong, B.; Antoni, J.; Pereira, Antonio; Kellermann, Walter (Journal of Sound and Vibration, 2016)
Blind separation of sound sources aims at reconstructing the individual sources which contribute to the overall radiation of an acoustical field. The challenge is to reach this goal using distant measurements when all sources are operating concurrently. The working assumption is usually that the sources of interest are incoherent – i.e. statistically orthogonal – so that their separation can be approached by decorrelating a set of simultaneous measurements, which amounts to diagonalizing the cross-spectral matrix. Principal Component Analysis (PCA) is traditionally used to this end. This paper reports two new findings in this context. First, a sufficient condition is established under which “virtual” sources returned by PCA coincide with true sources; it stipulates that the sources of interest should be not only incoherent but also spatially orthogonal. A particular case of this instance is met by spatially disjoint sources – i.e. with non-overlapping support sets. Second, based on this finding, a criterion that enforces both statistical and spatial orthogonality is proposed to blindly separate incoherent sound sources which radiate from disjoint domains. This criterion can be easily incorporated into acoustic imaging algorithms such as beamforming or acoustical holography to identify sound sources of different origins. The proposed methodology is validated on laboratory experiments. In particular, the separation of aeroacoustic sources is demonstrated in a wind tunne
LMS::1703
Keywords: Blind source separation; Source identification; Inverse problem; Joint approximate diagonalization

Blind Source Separation and Binaural Reproduction with Hearing Aids: An Overview - Wehr, Stefan; Puder, H.; Kellermann, Walter (Proc. ITG Conference on Speech Communication, 2008)
This paper addresses the problem of recovering or preserving spatial cues after microphone array processing by blind source separation. Based on the inherent identification of the unknown mixing system by blind source separation, we present several kinds of binaural reproduction algorithms. Firstly, we use the approximated mixing system for binaural reproduction by post-filtering. We are thus able to recover the spatial cues of the target source. Unfortunately, attenuated interfering sources are perceived from the direction of the target source. In order to achieve spatially correct binaural reproduction of interfering sources, we consider an adaptive post-processing algorith
LMS::772
Keywords: variable speed drives

Blind Source Separation for Convolutive Mixtures: A Unified Treatment - Buchner, Herbert; Aichner, Robert; Kellermann, Walter (Audio Signal Processing for Next-Generation Multimedia Communication Systems, 2004)

Blind Source Separation for Convolutive Mixtures Exploiting Nongaussianity, Nonwhiteness, and Nonstationarity - Buchner, Herbert; Aichner, Robert; Kellermann, Walter (Conf. Rec. Intl. Workshop on Acoustic Echo and Noise Control (IWAENC), 2003)
LMS::438

Blind system identification using sparse learning for TDOA estimation of room reflections - Kowalczyk, Konrad; Habets, Emanuël; Kellermann, Walter; Naylor, Patrick A. (IEEE Signal Processing Letters, 2013)
Localization of early room reflections can be achieved by estimating the time-differences-of-arrival (TDOAs) of reflected waves between elements of a microphone array. For an unknown source, we propose to apply sparse blind system identification (BSI) methods to identify the acoustic impulse responses, from which the TDOAs of temporally sparse reflections are estimated. The proposed time-and frequency-domain adaptive algorithms based on crossrelation formulation are regularized by incorporating an l-norm sparseness constraint, which is realized using a split Bregman method. These algorithms are shown to outperform standard crossrelation-based BSI techniques when estimating TDOAs of reflections in the presence of background noise. © 1994-2012 IEE
Keywords: Blind system identification; Bregman method; crossrelation error; sparse learning; time delay estimation

Blind Watermarking Applied to Image Authentication - Eggers, Joachim; Girod, Bernd (Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2001)
LMS::204

Block-Based Hybrid Video Coding Using Motion-Compensated Long-Term Memory Prediction - Wiegand, Thomas; Zhang, Z.; Girod, Bernd (Proc. Picture Coding Symposium, 1997)
LMS::424

Block Based Physical Modeling - Petrausch, Stefan (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2007)

Block-Based Physical Modeling - Rabenstein, Rudolf; Petrausch, Stefan (Proc. 5th Vienna Symposium on Mathematical Modeling, 2006)
LMS::599

Block-Based Physical Modeling for Digital Sound Synthesis - Rabenstein, Rudolf; Petrausch, Stefan; Sarti, Augusto; De Sanctis, Giovanni; Erkut, Cumhur; Karjalainen, Matti (IEEE Signal Processing Magazine, 2007)
Research and physical modeling digital sound synthesis has created a multitude of different models for all kinds of components of musical instruments. With this variety, it is now more difficult to build a multicomponent structure from different kinds of block models. Binary connection tree and the (BCT) block compiler (BC) allow the building of complex synthesis structures from a library of different kinds of component models without bothering the user with problems of block compatibility. The results are automatically generated synthesis algorithms for real-time operation, interactive human control and parameter variations with low latenc

Block-based physical modeling for digital sound synthesis of membranes and plates - Petrausch, Stefan; Rabenstein, Rudolf (International Computer Music Conference, ICMC 2005, 2005)
An application of block-based physical modeling for digital sound synthesis is presented in this paper. A membrane model in terms of a fourth order, two-dimensional partial differential equation is solved and discretized with the functional transformation method. The resulting algorithm constitutes an input-output description with input force and output velocity of the membrane. This input-output port is transformed to wave variables, such that the membrane model can be connected to an arbitrary wave digital filter network that includes nonlinear elements. The construction of these new interaction networks allows block-based physical modeling with mixed models. A real-time sound synthesis program is demonstrated, which simulates almost arbitrary two-dimensional resonators (e.g. membranes, wooden plates, metal plates) with almost arbitrary excitations (e.g. plucking, hammering). These sounds are produced only by changes of the physical parameters of the mode

Block-based physical modeling with applications in musical acoustics - Rabenstein, Rudolf; Petrausch, Stefan (International Journal of Applied Mathematics and Computer Science, 2008)
Block-based physical modeling is a methodology for modeling physical systems with different subsystems. Each subsystem may be modeled according to a different paradigm. Connecting systems of diverse nature in the discrete-time domain requires a unified interconnection strategy. Such a strategy is provided by the well-known wave digital principle, which had been introduced initially for the design of digital filters. It serves as a starting point for the more general idea of block-based physical modeling, where arbitrary discrete-time state space representations can communicate via wave variables. An example in musical acoustics shows the application of block-based modeling to multidimensional physical system
Keywords: Physical modeling; Sound synthesis; Wave digital filters

Block-based Physical Models Derived from Boundary Value Problems - Rabenstein, Rudolf; Petrausch, Stefan (Proc. 6th Vienna Conference on Mathematical Modelling, 2009)

BM3D-verfeinerte Zwischenbildsynthese für unterschiedlich aufgelöste Referenzansichten - Richter, Thomas; Wige, Eugen; Kaup, André (15. ITG-Fachtagung für Elektronische Medien, 2013)
Die Synthese unbekannter Zwischenansichten mit Hilfe von Tiefenkarten ist eine Kernaufgabe in der 3DVideosignalverarbeitung. Um ein hochqualitatives Zwischenbild generieren zu können, werden gewöhnlich mehrere hochaufgelöste Referenzansichten verwendet. Prinzipiell werden dabei die beiden benachbarten Kameraansichten mit Hilfe der entsprechenden Tiefeninformation in die Bildebene der Zielansicht projiziert und anschließend zusammengefügt.Im Normalfall bleiben nach dem Zusammenfügen der beiden Projektionsergebnisse nur noch sehr kleine Lochstrukturen übrig, die mit unterschiedlichen Interpolationsmethoden geschlossen werden können. Um die Kosten im Bezug auf das genutzte Kameraarray zu senken, könnten einige der hochauflösenden Kameras durch niedrigaufgelöste ersetzt werden. Diese niedrigauflösenden Kameras sind zwar billiger in der Anschaffung, liefern im Gegenzug aber auch eine deutlich schlechtere Bildqualität und beeinflussen damit das Ergebnis der Zwischenbildsynthese, besonders in Bildbereichen, die nur in der niedrigaufgelösten Referenz sichtbar sind. In diesem Beitrag wird die Fragestellung behandelt, wie ein Zwischenbild hoher Qualität aus zwei Kameraansichten generiert werden kann, die stark unterschiedliche Auflösungen und damit Bilder unterschiedlicher visueller Qualität liefern. Das Hauptaugenmerk dieser Arbeit liegt auf einem Nachverarbeitungsschritt, basierend auf dem BM3D-Verfahren, mit dem Ziel, Bildbereiche, die aus der niedrigaufgelösten Referenzansicht eingefügt wurden, an ihre hochaufgelöste Umgebung anzupassen. Die Simulationsergebnisse zeigen, dass die BM3D-Verfeinerung von synthetisierten Zwischenbildern zu einem PSNR-Gewinn von bis zu 0,51 dB auf dem gesamten Bild und 5,69 dB bezogen auf die Verfeinerungspositionen führt. Auch die visuelle Bildqualität lässt sichdurch die vorgeschlagene Nachverarbeitung deutlich steiger

Boosting based object detection using a geometric model - Quast, Katharina; Seeger, Christoph; Trivedi, Mohan; Kaup, André (2011 18th IEEE International Conference on Image Processing, ICIP 2011, 2011)
In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data. © 2011 IEE
Keywords: boosting; object detection; Object recognition

Boosting Compressed Sensing Using Local Measurements and Sliding Window Reconstruction - Grosche, Simon; Regensky, Andy; Seiler, Jürgen; Kaup, André (IEEE Transactions on Image Processing, 2020)

Boosting Neural Image Compression for Machines Using Latent Space Masking - Fischer, Kristian; Brand, Fabian; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2022)
Today, many image coding scenarios do not have a human as final intended user, but rather a machine fulfilling computer vision tasks on the decoded image. Thereby, the primary goal is not to keep visual quality but maintain the task accuracy of the machine for a given bitrate. Due to the tremendous progress of deep neural networks setting benchmarking results, mostly neural networks are employed to solve the analysis tasks at the decoder side. Moreover, neural networks have also found their way into the field of image compression recently. These two developments allow for an end-to-end training of the neural compression network for an analysis network as information sink. Therefore, we first roll out such a training with a task-specific loss to enhance the coding performance of neural compression networks. Compared to the standard VVC, 41.4% of bitrate are saved by this method for Mask R-CNN as analysis network on the uncompressed Cityscapes dataset. As a main contribution, we propose LSMnet, a network that runs in parallel to the encoder network and masks out elements of the latent space that are presumably not required for the analysis network. By this approach, additional 27.3% of bitrate are saved compared to the basic neural compression network optimized with the task loss. In addition, we are the first to utilize a feature-based distortion in the training loss within the context of machine-to-machine communication, which allows for a training without annotated data. We provide extensive analyses on the Cityscapes dataset including cross-evaluation with different analysis networks and present exemplary visual results. Inference code and pre-trained models are published at https://github.com/FAU-LMS/NCN_for_M2
Keywords: Video Coding for Machines; Neural Network Compression; Computer Vision; Learned Image Coding; Machine-to-Machine Communication

Bow-tie effect: differential operator - Castro, A.; Ojeda-Cstameda, J.; Lohmann, Adolf (Applied Optics, 2006)
We propose to use a differential operator for representing the influence of phase-only filters on the defocused modulation transfer function of the clear pupil aperture. We present a phase-only filter that implements optically Taylor's theorem in phase space. We show numerical simulations of the modulation transfer functions and the images that can be obtained by using the proposed filter.© 2006 Optical Society of Ameri
LMS::646

Breitbandige adaptive MIMO-Filterung: Eine vereinheitlichte Behandlung und Anwendungen auf akustische Mensch-Maschine-Schnittstellen - Buchner, Herbert (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2010)

Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Riess, Christian (2018)

Broadband multizone sound rendering by jointly optimizing the sound pressure and particle velocity - Bürger, Michael; Hofmann, Christian; Kellermann, Walter (The Journal of the Acoustical Society of America, 2018)
In this paper, a recently proposed approach to multizone sound field synthesis, referred to as joint pressure and velocity matching (JPVM), is investigated analytically using a spherical harmonics representation of the sound field. The approach is motivated by the Kirchhoff–Helmholtz integral equation and aims at controlling the sound field inside the local listening zones by evoking the sound pressure and particle velocity on surrounding contours. Based on the findings of the modal analysis, an improved version of JPVM is proposed, which provides both better performance and lower complexity. In particular, it is shown analytically that the optimization of the tangential component of the particle velocity vector, as is done in the original JPVM approach, is very susceptible to errors and thus not pursued anymore. Furthermore, the analysis provides fundamental insights as to how the spherical harmonics used to describe three-dimensional sound fields translate into two-dimensional basis functions as observed on the contours surrounding the zones. By means of simulations, it is verified that discarding the tangential component of the particle velocity vector ultimately leads to an improved performance. Finally, the impact of sensor noise on the reproduction performance is assessed.

BSS for improved interference estimation for blind speech signal extraction with two microphones - Zheng, Yuanhang; Reindl, Klaus; Kellermann, Walter (2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009, 2009)
Blind Source Extraction (BSE) as desirable for acoustic cocktail party scenarios requires estimates for the target or interfering signals. Conventional single-channel approaches for obtaining the interference estimate rely on noise and interference estimates during absence of the target signal. For multichannel approaches using multiple microphone signals, a separation of simultaneously active target and interference signals becomes possible if the positions of the target and interfering sources are known. We propose a new method which exploits Directional BSS (Blind Source Separation with a geometric constraint) to estimate the interfering speech sources and diffuse background noise jointly and blindly. Herewith we can effectively deal with the underdetermined BSS scenario (more point sources than sensors) in reverberant environments and can even allow for additional babble noise in the background. © 2009 IEE

Building a High Dynamic Range Video sensor with spatially non-regular optical filtering - Schöberl, Michael; Belz, A.; Nowak, Arne; Seiler, Jürgen; Kaup, André; Foessel, S. (Applications of Digital Image Processing XXXV, 2012)
Although we observe a steady progress in the development of High Dynamic Range Video (HDRV) technology, current image sensors are still lacking achievable dynamic range for high image quality applications. We propose a new imaging principle that is based on a spatial variation of optical Neutral Density (ND) filters on top of some pixels. In existing work, this method has been used to trade spatial resolution for an increase in dynamic range. We improve this approach by a non-regular placement of these filters. The non-regular sampling is an important step as any sub-sampling with regular patterns leads to aliasing. The non-regular patterns however preserve just a single dominant spatial frequency and enable an image reconstruction without aliasing. In combination with a new image reconstruction approach, we are able to recover image details at high resolution. The iterative reconstruction is based on the assumption that natural images can be represented with few coefficients in the Fourier domain. As typical natural images can be classified as near-sparse, the method enables the reconstruction of images of high objective and visual quality. In extension to theory and simulation of this method we want to present details on a practical implementation of our method. While building a demonstration system we encountered many challenges. This includes effects like crosstalk, aspects like sensor selection and mask fabrication as well as mounting of the masks. © 2012 SPI
Keywords: Digital cameras; High dynamic range video; Image acquisition; Neutral density filter

Calculation of the Transformation Kernels for the Functional Transformation Method - Schäfer, Maximilian; Rabenstein, Rudolf (10th International Workshop on Multidimensional (nD) Systems (nDS 2017), 2017)
The main challenge in the application of the Functional Transformation Method is the determination of the transformation kernels for the forward and inverse Sturm-Liouville transformation. This paper presents a straightforward way for their calculation from the eigenfunctions and eigenfrequencies of the underlying physical system. The proposed form of the eigenfunctions is based on a beneficial representation of the matrix exponential. A suitable approach for the calculation of eigenfrequencies is also presented. Two extended examples demonstrate the proposed algorithm

Calibrating visual sensors and actuators in distributed platforms - Hörster, Eva; Lienhart, R.; Kellermann, Walter; Bouguet, J. Y. (Computer Vision for Interactive and Intelligent Environments 2005, 2005)
Many novel multimedia, home entertainment, visual surveillance and health applications use multiple audio-visual sensors and actuators. In this paper we present a novel approach for position and pose calibration of visual sensors and actuators, i.e. cameras and displays, in a distributed network of general purpose computing devices. It complements our work on position calibration of audio sensors and actuators in a distributed computing platform [14]. The approach is suitable for a wide range of possible - even mobile - setups since (a) synchronization is not required, (b) it works automatically, (c) only weak restrictions are imposed on the positions of the cameras and displays, and (d) no upper limit on the number of cameras and displays under calibration is imposed. Corresponding points across different camera images are established automatically and found with subpixel accuracy. Cameras do not have to share one common view. Only a reasonable overlap between camera subgroups is necessary. The method has been sucessfully tested in numerous multi-camera environments with a varying number of cameras and displays and has proven itself to work extremely accurate. © 2005 IEE

Calibrating Visual Sensors and Actuators in Distributed Platforms - Horster, Eva; Lienhart, Rainer; Kellermann, Walter; Bouguet, Jean-Yves (Proceedings of Computer Vision for Interactive and Intelligent Environment (CVIIE'05), 2005)
Many novel multimedia, home entertainment, visual surveillance and health applications use multiple audio-visual sensors and actuators. In this paper we present a novel approach for position and pose calibration of visual sensors and actuators, i.e. cameras and displays, in a distributed network of general purpose computing devices. It complements our work on position calibration of audio sensors and actuators in a distributed computing platform [14]. The approach is suitable for a wide range of possible - even mobile - setups since (a) synchronization is not required, (b) it works automatically, (c) only weak restrictions are imposed on the positions of the cameras and displays, and (d) no upper limit on the number of cameras and displays under calibration is imposed. Corresponding points across different camera images are established automatically and found with subpixel accuracy. Cameras do not have to share one common view. Only a reasonable overlap between camera subgroups is necessary. The method has been sucessfully tested in numerous multi-camera environments with a varying number of cameras and displays and has proven itself to work extremely accurat
Keywords: Actuators; Cameras; Calibration; Auditory displays; Distributed computing; Multimedia systems; Surveillance; Computer displays; Computer networks

Calibration of visual sensors and actuators in distributed computing platforms - Hörster, Eva; Lienhart, R.; Kellermann, Walter; Bouguet, J. Y. (3rd ACM International Workshop on Video Surveillance and Sensor Networks, VSSN 2005, 2005)
Many novel multimedia and visual surveillance applications use multiple sensors and actuators. We present a novel approach for position calibration of visual sensors and actuators, i.e. cameras and displays, in a distributed network of general purpose computing devices. The proposed approach is very suitable for calibration in a wide range of setups since (a) synchronization is not required, (b) it works fully automatic, (c) only weak restrictions are imposed on the positions of the cameras and displays, and (d) no upper limit on the number of cameras and displays under calibration is imposed. Corresponding points across different camera images are established automatically and found with subpixel accuracy. Cameras do not have to share one common view, only a reasonable overlap between camera subgroups is necessary. The method has been sucessfully tested in numerous multi-camera environments with a varying number of cameras and displays. It has proven itself to work extremely accurate. Performance results are reporte
Keywords: Multi-camera calibration; Position calibration; Sensor networks

Camera Array for Multi-Spectral Imaging - Genser, Nils; Seiler, Jürgen; Kaup, André (IEEE Transactions on Image Processing, 2020)
Recently, many new applications arose for multi-spectral and hyper-spectral imaging. Besides modern biometric systems for identity verification, also agricultural and medical applications came up, which measure the health condition of plants and humans. Despite the growing demand, the acquisition of multi-spectral data is up to the present complicated. Often, expensive, inflexible, or low resolution acquisition setups are only obtainable for specific professional applications. To overcome these limitations, a novel camera array for multi-spectral imaging is presented in this article for generating consistent multi-spectral videos. As differing spectral images are acquired at various viewpoints, a geometrically constrained multi-camera sensor layout is introduced, which enables the formulation of novel registration and reconstruction algorithms to globally set up robust models. On average, the novel acquisition approach achieves a gain of 2.5 dB PSNR compared to recently published multi-spectral filter array imaging systems. At the same time, the proposed acquisition system ensures not only a superior spatial, but also a high spectral, and temporal resolution, while filters are flexibly exchangeable by the user depending on the application. Moreover, depth information is generated, so that 3D imaging applications, e.g., for augmented or virtual reality, become possible. The proposed camera array for multi-spectral imaging can be set up using off-the-shelf hardware, which allows for a compact design and employment in, e.g., mobile devices or drones, while being cost-effective.
CRIS-Team Scopus Importer:2020-10-23
Keywords: image acquisition; Multi-spectral imaging

CAMERA SELF-CALIBRATION: DEEP LEARNING FROM DRIVING SCENES - Rachman, Arya; Seiler, Jürgen; Kaup, André (Proceedings - International Conference on Image Processing, ICIP, 2022)
Prior to driving, cameras embedded in an autonomous driving system need to be calibrated intrinsically. Calibration is crucial to ensure that safety-related perception functions can reliably perceive the environment. Vehicle cameras are also exposed to mechanical perturbations requiring periodic re-calibration with regular uses. The current widely-accepted calibration approaches are based on robust but potentially demanding target-based methods. Such methods require a car to be taken offline and rely on static infrastructure and operators. Targetless online calibration approaches exist but remain largely unadopted due to the accuracy gaps compared to the classical methods. We propose a deep-learning-based self-calibration strategy for the vehicular camera that learns from driving scenes-they make an inherently large-scale dataset-and is validated back-to-back against checkerboard reprojection error. Our approach results in a 2.5% decrease in subpixel reprojection error compared to the existing deep-learning-based approaches. We also demonstrate its practical application in the automotive domain.
CRIS-Team Scopus Importer:2024-12-16
Keywords: autonomous vehicle; camera; deep learning; self-calibration

Capacity of Digital Watermarks Subjected to an Optimal Collusion Attack - Su, Jonathan; Eggers, Joachim; Girod, Bernd (X European Signal Processing Conference (EUSIPCO 2000), 2000)
LMS::195

Cardioid Pattern Optimization for a Virtual Circular Microphone Array - Kuntz, Achim; Rabenstein, Rudolf (EAA Symposium on Auralization, 2009)
A measurement system is presented that allows large apertur e measurements with a high angular resolution and that is still co mpact and thus convenient for portable use. Theoretical cons iderations show that a cardioid microphone characteristic is de sireable to achieve a low amplification of sensor noise throughou t the audio frequency range. The directivity of real cardioid microphones, however, deviates from the ideal cardioid pattern. Here, it is demonstrated how the use of one omnidirectional and one figure-of-eight microphone offers the possibility to optim ize the directivity pattern of a virtual cardioid microphone throu gh suitably designed compensation filter

Centroid adapted frequency selective extrapolation for reconstruction of lost image areas - Schnurrer, Wolfgang; Jonscher, Markus; Seiler, Jürgen; Richter, Thomas; Bätz, Michel; Kaup, André (IEEE International Conference on Visual Communications and Image Processing (VCIP), 2015)
Lost image areas with different size and arbitrary shape can occur in many scenarios such as error-prone communication, depth-based image rendering or motion compensated wavelet lifting. The goal of image reconstruction is to restore these lost image areas as close to the original as possible. Frequency selective extrapolation is a block-based method for efficiently reconstructing lost areas in images. So far, the actual shape of the lost area is not considered directly. We propose a centroid adaption to enhance the existing frequency selective extrapolation algorithm that takes the shape of lost areas into account. To enlarge the test set for evaluation we further propose a method to generate arbitrarily shaped lost areas. On our large test set, we obtain an average reconstruction gain of 1.29 d
Keywords: Error Concealment; Image Reconstruction; Signal Extrapolation; Wavelet Transform

Challenges in Acoustic Signal Enhancement for Human-Robot Communication - Löllmann, Heinrich; Barfuß, Hendrik; Deleforge, Antoine; Kellermann, Walter (11. ITG Fachtagung Sprachkommunikation, 2014)
The construction of a humanoid robot, which can communicate with humans in a natural manner, is a worthwhile and challenging task alike. This paper discusses some major difficulties encountered in acoustic signal acquisition by the widely used humanoid robot NAO and the implications for the design of the signal enhancement algorithms that are needed for human-robot communication. Measurements with this low-cost robot, whose microphones and loudspeakers are mounted in the head, show the challenges for the noise reduction and acoustic echo control (AEC) due to ego-noise and nonlinear loudspeaker characteristics. It is also discussed how peripheral microphones at the limbs of the robot can mitigate these problems and offer new prospects for multi-channel signal enhancemen
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Change Detection in Image Sequences Using Gibbs Random Fields: A Bayesian Approach - Aach, T.; Kaup, André; Mester, R. (IEEE International Workshop on Intelligent Signal Processing and Communication Systems, Sendai, 1993)
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Change detection in JPEG 2000 compressed video - Schöberl, Michael; Bruns, V.; Foessel, S.; Bloss, H.; Kaup, André (Electronics Letters, 2010)
A method for detecting scene changes from JPEG 2000 compressed images is presented. Change information and localisation is derived from changes in the local data rate. They can be extracted by analysing the code streams only. The computationally expensive decoding of image data is eliminated. © 2010 The Institution of Engineering and Technolog

Channel Model for a Watermark Attack - Su, Jonathan; Hartung, Frank; Girod, Bernd (Proc. SPIE Security and Watermarking of Multimedia Contents, 1999)
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Channel Modeling for Drug Carrier Matrices - Schäfer, Maximilian; Salinas, Yolanda; Ruderer, Alexander; Enzenhofer, Franz; Brueggemann, Oliver; Schober, Robert; Haselmayr, Werner (2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021)
CRIS-Team WoS Importer:2022-05-27

Channel Responses for the Molecule Release from Spherical Homogeneous Matrix Carriers - Schäfer, Maximilian; Salinas, Yolanda; Enzenhofer, Franz; Bruggemann, Oliver; Martinez-Manez, Ramon; Rabenstein, Rudolf; Schober, Robert; Haselmayr, Werner; Ruderer, Alexander (IEEE Transactions on Molecular, Biological and Multi-Scale Communications, 2022)
Molecular communications is a promising framework for the design of controlled-release drug delivery systems. Under this framework, drug carriers, diseased cells, and the channel in between are modeled as transmitters, absorbing receivers, and diffusive channel, respectively. However, existing works on drug delivery systems consider only simple drug carrier models, which limits their practical applicability. In this paper, we investigate diffusion-based spherical matrix-type drug carriers, which are employed in medical applications. In a matrix carrier, the drug molecules are dispersed in the matrix core and diffuse from the inner to the outer layers of the carrier once immersed in a dissolution medium. We derive the channel response of the matrix carrier transmitter for an absorbing receiver. The results are validated by particle-based simulations and compared with commonly used point and transparent spherical transmitters to highlight the necessity of considering practical models. Moreover, we show that a transparent spherical transmitter, with the drug molecules uniformly distributed over the entire volume, is a special case of the considered matrix system. For this case, we provide an analytical expression for the channel response. Furthermore, we derive a criterion for evaluating whether the release process or the channel dynamics are more important for the overall characteristics of the channel response of a drug delivery system. For the limiting regimes, where only the release process or only the channel determine the behavior of the end-to-end system, we propose closed-form approximations for the channel response. Finally, as a scenario of practical relevance, we investigate the channel responses for the release of common therapeutic drugs, e.g., doxorubicin, from a diblock copolymer micelle acting as drug carrier.
Created from Fastlane, Scopus look-up
Keywords: Analytical models; Biological system modeling; Biology; Diseases; Drugs; Polymers; Transmitters

Chapter 4 - Recent advances on LIP nonlinear filters and their applications: Efficient solutions and significance-aware filtering - Hofmann, Christian; Kellermann, Walter (Adaptive learning methods for nonlinear system modeling, 2018)
Linear-In-the-Parameters (LIP) nonlinear filters are categorized as Cascade Models (CMs) (generalizing Hammerstein models), Cascade Group Models (CGMs) (generalizing Hammerstein Group models (HGMs) and including, e.g., Volterra filters) and bilinear cascade models, where the filter output is a bilinear function of the model parameters. Time-domain and partitioned-block frequency-domain adaptation of CGMs and CMs is described and the methods for adapting bilinear cascade models are summarized as variants of the filtered-X adaptation. These models and algorithms are employed to review the Significance-Aware (SA) filtering concept, decomposing the model for the unknown system and the adaptation mechanism into synergetic subsystems to achieve high computational efficiency. In particular, the Serial SA (SSA) and Parallel SA (PSA) decomposition lead to SSA-CMs, PSA-CGMs and a novel PSA filtered-X algorithm. The main concepts described in this chapter are exemplarily compared for the challenging application of nonlinear acoustic echo cancellation. Furthermore, model structure estimation for LIP nonlinear filters based on convex filter combinations is briefly outlined and compared with SA filterin
Keywords: Cascade models; Cascade group models; Hammerstein models; Hammerstein group models; Significance-aware filtering; SA filtering; Filtered-X

Characteristics of Audio Streaming over IP networks within the ISMA Standard - Grill, B.; Hahn, T.; Homm, D.; Krauss, K.; Ohler, G.; Sörgel, Wolfgang; Spitzner, C. (Audio Engineering Society Convention Paper, 2002)
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Characterization of Acoustical Environments by Numerical Simulation - Spors, Sascha; Rabenstein, Rudolf (7th International Workshop on Acoustic Echo and Noise Control, 2001)
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Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data - Kreuzer, Matthias; Schmidt, David; Wokusch, Simon; Kellermann, Walter (2024 IEEE International Conference on Prognostics and Health Management (ICPHM), 2024)
In this paper, we address the task of classifying rotor imbalances in induction motors of trains by analysing airborne sound data. We propose the use of a residual-based Convolutional Neural Network (CNN) architecture that stands out for its small number of trainable parameters. The proposed model is evaluated with real-world data that was gathered from state-of-the-art metro trains during regular operation. The experiments show that with the proposed approach rotor imbalances can be reliably classified even when the model is confronted with new damages that were not included in training.
CRIS-Team Scopus Importer:2024-09-06
Keywords: airborne sound classification; condition monitoring; real-world data; rotor imbalance; train

Classification of time delay estimates for robust speaker localization - Strobel, Norbert; Rabenstein, Rudolf (Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99), 1999)
This paper proposes a solution to the problem of robust speaker localization under adverse acoustic conditions. The approach is based on the classification of time delay estimates. Two classification techniques are investigated in detail: maximum likelihood (ML) classification and classification based on histogram comparison. Their performance under adverse acoustic conditions is compared to outcomes obtained with the traditional approach which uses time delay estimates directly to infer speaker positions. Experiments indicate that the ML classification method provides little improvement over the traditional method. On the other hand, using histogram classification, we can improve the probability of correct speaker localization by more than 60% compared to either the traditional approach or the ML classification technique.

Closed-form estimation of the speed of propagating waves from time measurements - Annibale, Paolo; Rabenstein, Rudolf (Multidimensional Systems and Signal Processing, 2014)
The propagating speed of waves depends on the physical properties of the transmitting material. Since these properties can vary along the propagation path, they cannot be determined from local measurements. However, mean values of the propagation speed can be obtained from time measurements, either between distributed sources and sensors (Time Of Arrival, TOA) if both are synchronized or otherwise from time differences between distributed sensors (Time Difference Of Arrivals, TDOA). This contribution investigates the required assumptions for speed estimation from time measurements and provides closedform solutions for the synchronized and unsynchronized case. Furthermore the achievable accuracy is determined in terms of Cramer-Rao bounds. The analysis is carried out for the propagation of sound waves in air, where the propagation speed varies with the air temperature. Example results from loudspeaker-microphone recordings are provided. However the closed-form relations apply also to the propagation of other types of waves in linear regimes. This manuscript extends previous work by the authors by providing closed-form solutions and by a parallel treatment of the TOA and the TDOA measurements. © Springer Science+Business Media New York 201
Keywords: Closed-form; Propagation speed; Source localization; Speed of sound; Time difference of arrivals; Time of arrival

Coding of Distortion-Corrected Fisheye Video Sequences Using H.265/HEVC - Eichenseer, Andrea; Kaup, André (Proc. of IEEE International Conference on Image Processing (ICIP), 2014)
Images and videos captured by fisheye cameras exhibit strong radial distortions due to their large field of view. Conventional intra-frame as well as inter-frame prediction techniques as employed in hybrid video coding schemes are not designed to cope with such distortions, however. So far, captured fish-eye data has been coded and stored without consideration to any loss in efficiency resulting from radial distortion. This paper investigates the effects on the coding efficiency when applying distortion correction as a pre-processing step as opposed to the state-of-the-art method of post-processing. Both methods make use of the latest video coding standard H.265/HEVC and are compared with regard to objective as well as subjective video quality. It is shown that a maximum PSNR gain of 1.91 dB for intra-frame and 1.37 dB for inter-frame coding is achieved when using the pre-processing method. Average gains amount to 1.16 dB and 0.95 dB for intra-frame and inter-frame coding, respectivel
UnivIS-Import:2015-04-16:Pub.2014.tech.IE.LEN.coding
Keywords: GRK-1773

Coding of Segmented Images Using Shape-Independent Basis Functions - Kaup, André; Aach, T. (IEEE Transactions on Image Processing, 1998)
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Coding Techniques for Multi-View Video Signals, Dissertation - Fecker, Ulrich (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2009)
Multi-view video is a technique where a moving object or a scene is recorded using a setup of several synchronous cameras. By applying image-based rendering techniques, new views can be generated for arbitrary viewpoints and viewing angles. Even complex lighting conditions and surface characteristics are reproduced in a photorealistic quality. Furthermore, the computational effort is independent from the scene complexity. Applications include three-dimensional television, where the viewer can choose his viewing position and viewing angle freely, as well as medical visualisations. However, recording multi-view video signals leads to massive amounts of data. That is why efficient compression is crucial for making practical systems possible. The aim of this thesis is on the one hand the efficient coding of multi-view video data and on the other hand the development of pre- and post-filtering techniques which improve the compression and the rendering result. Instead of coding each camera view separately, the correlation between neighbouring views is exploited. The achievable gain by doing so is estimated through a statistical analysis. Furthermore, the standardisation efforts of the Joint Video Team (JVT) regarding multi-view coding are taken into account. Further aspects which are investigated include random access to the coded data and the compression of depth maps, which are frequently used as side informatio

Coefficient-dependent step-size for adaptive second-order Volterra filters - Küch, Fabian; Kellermann, Walter (12th European Signal Processing Conference, EUSIPCO 2004, 2004)
In this contribution we propose a coefficient-dependent and time-variant step-size for the least mean square (LMS) algorithm applied to adaptive second-order Volterra filters. The optimum step-size is derived by introducing a novel optimality criterion which is given by the minimum mean squared error between the coefficient error of an adaptive Volterra filter coefficient and the respective LMS update term of that coefficient. As the optimum step-size includes statistical terms that are in general not accessible, we also present models for estimating these quantities for the application in nonlinear acoustic echo cancellation.

Coefficient Pruning for Higher-Order Diagonals of Volterra Filters Representing Wiener-Hammerstein Models - Zeller, Marcus; Kellermann, Walter (Proc. Int. Workshop on Acoustic Echo and Noise Control (IWAENC), 2008)
Block-based nonlinear structures as, e.g., the Wiener-Hammer- stein model, are popular for analyzing a broad class of nonlinear distortions. On the other hand, these models can also be related to Volterra filters in diagonal coordinates which represent very general nonlinear filters with memory. In this work, we propose an approach for estimating the significant coefficients of the nonlinear Volterra kernels during adaptation of such block-based filters by relating the linear impulse response and the diagonals of higher-order kernels. As the number of coefficients for the higher-order kernels is gener- ally very large, this information can then be used to prune the num- ber of necessary coefficients and thus lower the computational com- plexity. Experimental results for several test signals and nonlinear systems demonstrate the effectiveness of such an approach in a non- linear acoustic echo cancellation scenari
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Coherence-aware stereophonic residual echo estimation - Luis Valero, Maria; Yildiz, Ilkay; Mabande, Edwin; Habets, Emanuël (Proc. of the Joint Workshop on Hands-free Speech Communication and Microphone Arrays, 2017)
The performance of stereophonic acoustic echo cancelers is highly dependent on the coherence between the loudspeaker signals. If this is high, convergence is slow, during which audible residual echoes remain. Residual echoes can be reduced by applying a residual echo suppressor to the canceler's output. Yet, in order to obtain the post-filter gains, it is necessary to estimate the residual echoes first, and the performance of stereophonic residual echo estimators also depends on the loudspeaker signals' coherence. In this paper, a stereophonic residual echo estimator is proposed that takes the coherence between the signals into account. By doing this, it is possible to reliably estimate the residual echoes, even if the loudspeaker signals are completely coheren
Keywords: Coherence estimation; Stereophonic acoustic echo cancellation; Residual echo suppression

Coherence-based dereverberation for automatic speech recognition - Schwarz, Andreas; Brendel, Andreas; Kellermann, Walter (Deutsche Jahrestagung für Akustik (DAGA), 2014)
The idea of performing dereverberation using a short-time spatial coherence estimate dates back to 1977 [1], when it was proposed to essentially use the magnitude of the coherence as gain for reverberation suppression. Another heuristic method was recently proposed in [2], where a soft threshold function is used to compute a gain from the coherence magnitude, and the parameters of the threshold function are adapted depending on the histogram of the coherence magnitude in each frequency bin. Short-time coherence estimates have also been investigated in the context of beamforming as a so-called postfilter, and solutions for supression of uncorrelated and diffuse noise have been proposed [3]. In this contribution, we focus on methods where, first, the ratio between direct and reverberation signal components (coherent-to-diffuse ratio, CDR) is estimated from a short-time coherence estimate, and filter weights for reverberation suppression are computed from the CDR using, e.g., the Wiener filter or spectral subtraction rule. We compare and illustrate the behavior of a number of different CDR estimators that have been proposed over the past years, and propose a new variant. Finally, we compare the practical effect of the methods by processing reverberated speech and evaluating the recognition accuracy achieved by an automatic speech recognizer with the processed signals.

Coherent-to-diffuse power ratio estimation for dereverberation - Schwarz, Andreas; Kellermann, Walter (IEEE Transactions on Audio Speech and Language Processing, 2015)
The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberatio
Keywords: Dereverberation; diffuse noise suppression; diffuseness; reverberation suppression; spatial coherence

Color Agnostic Cross-Spectral Disparity Estimation - Sippel, Frank; Genser, Nils; Och, Hannah; Seiler, Jürgen; Kaup, André (2024 IEEE International Conference on Acoustics, Speech and Signal Processing, 2024)

Color Channel Reconstruction for Multi-Color Multi-View Images Using Disparity and Color Similarity-based Local Linear Regression - Kiesel, Daniel; Richter, Thomas; Seiler, Jürgen; Kaup, André (IS&T Electronic Imaging - Image Sensors and Imaging Systems Conference, 2018)

Color constancy and non-uniform illumination: Can existing algorithms work? - Bleier, Michael; Riess, Christian; Beigpour, S.; Eibenberger, E.; Angelopoulou, E.; Tröger, Tobias; Kaup, André (2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, 2011)
The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression. © 2011 IEE

Combination of adaptive feedback cancellation and binaural adaptive filtering in hearing aids - Lombard, Anthony; Reindl, Klaus; Kellermann, Walter (EURASIP Journal on Advances in Signal Processing, 2009)
We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures. Copyright © 2009 2009 Anthony Lombard et a

Combination of Robust Adaptive Beamforming with Acoustic Echo Cancellation for Acoustic Human/Machine Interfaces - Herbordt, Wolfgang (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2004)

Combined Displacement Estimation and Segmentation of Stereo Image Pairs based on Gibbs Random Fields - Aach, T.; Kaup, André; Mester, R. (IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, 1990)
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Combined LCMV-TRINICON beamforming for separating multiple speech sources in noisy and reverberant environments - Markovich-Golan, Shmulik; Gannot, S.; Kellermann, Walter (IEEE/ACM Transactions on Audio, Speech and Language Processing, 2017)
he problem of source separation using an array of microphones in reverberant and noisy conditions is addressed. We consider applying the well-known linearly constrained minimum variance (LCMV) beamformer (BF) for extracting individual speakers. Constraints are defined using relative transfer functions (RTFs) for the sources, which are ratios of acoustic transfer functions (ATFs) between any microphone and a reference microphone. The latter are usually estimated by methods that rely on single-talk time segments where only a single source is active and on reliable knowledge of the source activity. Two novel algorithms for estimation of RTFs using the “Triple N” ICA for convolutive mixtures (TRINICON) framework are proposed, not resorting to the usually unavailable source activity pattern. The first algorithm estimates the RTFs of the sources by applying multiple two-channel geometrically constrained (GC) TRINICON units, where approximate direction of arrival information for the sources is utilized for ensuring convergence to the desired solution. The GC-TRINICON is applied to all microphone pairs using a common reference microphone. In the second algorithm, we propose to estimate RTFs iteratively using GC-TRINICON, where instead of using a fixed reference microphone as before, we suggest to use the output signals of LCMV-BFs from the previous iteration as spatially processed references with improved signal-to-interference-and-noise ratio. For both algorithms, a simple detection of noise-only time segments is required for estimating the covariance matrix of noise and interference. We conduct an experimental study in which the performance of the proposed methods is confirmed and compared to corresponding supervised method
LMS::1709
Keywords: Blind source separation; LCMV; relative transfer function; voice activity

Combined nonlinear echo cancellation and residual echo suppression - Schwarz, Andreas; Hofmann, Christian; Kellermann, Walter (11. ITG Symposium on Speech Communication, 2014)
We describe a combined nonlinear acoustic echo cancellation and residual echo suppression system. The echo canceler uses parallel Hammerstein branches consisting of fixed nonlinear basis functions and linear adaptive filters. The residual echo suppressor uses an Artificial Neural Network for modeling of the residual echo spectrum from spectral features computed from the far-end signal. We show that modeling nonlinear effects both in the echo canceler and in the echo suppressor leads to an increased performance of the combined syste

Combined-order hidden Markov models for reverberation-robust speech recognition - Maas, Roland; Kotha, Sujan Reddy; Sehr, Armin; Kellermann, Walter (3rd International Workshop on Cognitive Information Processing (CIP), 2012)
In this contribution, the concept of combined-order hidden Markov models (CO-HMMs) is introduced by joining the first-order Markov and the second-order conditional independence assumption. The proposed approach is motivated and evaluated in the context of reverberation-robust automatic speech recognition. Two predecessor-dependent output probability density functions per hidden Markov model (HMM) state are employed in order to explicitly cope with the high inter-frame correlation in presence of reverberation. At the same time, the state duration modeling related to the first-order Markov assumption is addressed by a recently published training procedure based on hard alignment having the significant advantage that any conventional HMM can be efficiently updated to a CO-HMM. The experimental results show a reduction in average entropy as well as in word error rate in reverberant environments compared to conventional HMMs. © 2012 IEE

Combining Adaptive Filtering and Complex-valued Deep Postfiltering for Acoustic Echo Cancellation - Halimeh, Mhd Modar; Haubner, Thomas; Briegleb, Annika; Schmidt, Alexander; Kellermann, Walter (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021)

Combining digital waveguide and functional transformation methods for physical modeling of musical instruments - Trautmann, Lutz; Välimäki, V.; Bank, B.; Rabenstein, Rudolf (Proc. AES 22nd Int. Conf. on Virtual, Synthetic and Entertainment Audio, 2002)
Digital sound synthesis based on physical models is realized in real-time applications mostly with the well known digital waveguide method (DWG). It approximates the underlying physical behavior of a vibrating structure in a computationally efficient way. Due to these computational efficient approximations, the waveguide method looses the direct connection to the parameters of the underlying physical model. The recently introduced functional transformation method (FTM) on the other hand solves the underlying physical model analytically. Thus, the physical parameters are explicitly given in the discrete realization of the FTM. But due to this 'physicality' the computational cost of synthesis using FTM is larger than using DWG. This paper compares the DWG with the FTM and shows that for linear vibrating strings it is always possible to design an acoustically indistinguishable DWG approximation with the parameters obtained from the FTM. In that way, a computationally efficient and physically meaningful synthesis method is obtained. Furthermore, this paper shows the limits of this new synthesis method.

Combining Monaural Beamforming and Blind Source Separation for Binaural Speech Enhancement in Multi-Microphone Hearing Aids - Reindl, Klaus; Prokein, P.; Fischer, E.; Zheng, Yuanhang; Kellermann, Walter (Proc. ITG Conference on Speech Communication, 2010)
A binaural speech enhancement concept based on blind source separation (BSS) formulti-microphone hearing aids is introduced. The proposed concept combines monaural differential beamformers and a blind binaural speech enhancement strategy for improved noise suppression. In contrast to a BSS-based speech enhancement strategy with only two microphones, this scheme solves the front-back ambiguity of two-microphone binaural systems. This novel binaural speech enhancement concept is analyzed and evaluated for different scenarios. The proposed combination turns out to be an efficient concept for binaural suppression of interfering point sources as well as background nois

Combining Run-Length Encoding Preprocessing With Lempel-Ziv-Markov Algorithm to Enhance Compression for Automotive Radar Data - Rückert, Rainer; Herglotz, Christian; Sura, Oliver; Ninov, Youliy; Kaup, André; Vossiek, Martin (2025 16th German Microwave Conference (GeMiC), 2025)

Combining single-image and multiview super-resolution for mixed-resolution image plus depth data - Richter, Thomas; Seiler, Jürgen; Schnurrer, Wolfgang; Bätz, Michel; Kaup, André (23rd European Signal Processing Conference, EUSIPCO 2015, 2015)
In mixed-resolution multiview setups, a scene is captured from various viewpoints with cameras having different spatial resolutions. Compared to full-resolution systems, mixed-resolution setups allow for savings with respect to data transmission, storage, and costs. However, for applications like free viewpoint television, high-quality images are required for all available camera perspectives. Therefore, high-resolution cameras can be used to increase the image quality of a neighboring low-resolution view. Due to occlusions, some parts of the scene are invisible in the high-resolution reference views and thus cannot be directly synthesized from the neighboring perspectives. In this paper, we propose to integrate the idea of single-image super-resolution to better handle occluded areas and thus to improve the super-resolution quality for mixed-resolution multiview images. For a downsampling factor of 4, the proposed method achieves an average gain of 0.53 dB with respect to a comparable multiview super-resolution approac
Keywords: Mixed-Resolution; Multiview; Super-Resolution

Comparative image quality assessment using free energy minimization - Zhai, Guangtao; Kaup, André (2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, 2013)
It is a straightforward task for human observers to judge the relative quality of two visual signals of the same content, but subject to different type/level of distortions. However, this comparative image quality assessment (C-IQA) problem remains a difficult challenge for the current research of image quality assessment (IQA). In this paper, we propose a C-IQA approach to predict the relative perceptual quality of a pair of images that are possibly subject to different artifact types/levels. The C-IQA algorithm is designed to emulate the process of comparing the relative quality of two visual stimuli as performed by the human visual system (HVS) within the framework of free energy minimization. The brain's internal generative models initialized on the inputs are used to explain the two images. And their relative quality can then be determined through comparing the free energy level of this model-data fitting process. In the existing work of IQA, the full-reference (FR) and reduced-reference (RR) methods need the prior knowledge of the original images while the no-reference (NR) algorithms usually work with a single input image. The C-IQA approach is inherently different from those existing methods in that it takes as input an image pair and predicts their relative quality without using any knowledge about the original image. A computationally efficient solution to the proposed C-IQA scheme based on a linear autoregressive image model is also introduced. Experimental results show that the proposed method achieves about 98% accuracy in line with the subjective ratings when applied on over 300,000 image pairs sampled from the LIVE database, outperforming the FR metrics such as PSNR, SSIM, and some of the most advanced NR IQA algorithms. © 2013 IEE

Comparative Study of Single-Channel Algorithms for Blind Reverberation Time Estimation - Löllmann, Heinrich; Brendel, Andreas; Kellermann, Walter (International Congress on Acoustics (ICA), 2019)
Knowledge about the reverberation time (T60) is exploited by numerous algorithms, e.g., for automatic speech recognition, speech dereverberation, or as a feature for acoustic scene classification. This contribution provides a comprehensive survey about various state-of-the-art methods to estimate the reverberation time blindly from a noisy and reverberant speech signal and compares their estimation performance for different acoustic scenarios. The evaluation considers different acoustic conditions regarding the signal-to-noise ratio (SNR), direct-to-reverberation ratio (DRR) and T60, and uses databases with room impulse responses measured in different acoustic enclosures. The findings of the Acoustic Characterisation of Environments (ACE) Challenge are extended by taking into account algorithms and acoustic scenarios which are not considered in this benchmarking campaign, and by using a much larger set of reverberant speech signals.<br
Keywords: Reverberation time; blind estimation; single-channel; survey

Comparison and a Theoretical Link Between Time-Domain and Frequency-Domain Blind Source Separation - Aichner, Robert; Buchner, Herbert; Kellermann, Walter (Proc. 29th Annual German Conf. on Acoustics (DAGA), 2003)
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Comparison of Matching Strategies for Temporal Frame Registration in the Perceptual Evaluation of Video Quality - Barkowsky, Marcus; Bitto, R.; Bialkowski, Jens; Kaup, André (2nd International Workshop on Video Processing and Quality Metrics for Consumer Electronics, 2006)
In this paper we compare the performance of different full-frame and block-based algorithms for the temporal alignment of two video sequences. The setup is typical for full reference video quality estimation in a low-bitrate scenario. Lossless and lossy digital transmission scenarios are combined with different distor- tions usually found in playback devices. The results show that the choice of the algorithm for temporal registration depends very much on the type of additional distortion expecte
LMS::594

Comparison of subspace-based and steered beamformer-based reflection localization methods - Mabande, Edwin; Sun, Haohai; Kowalczyk, Konrad; Kellermann, Walter (19th European Signal Processing Conference, EUSIPCO 2011, 2011)
This paper presents a comparative study of several techniques that are applicable to the localization of the most significant reflectors in an acoustic environment. It focuses on subspace-based and steered beamformer-based localization techniques and includes eigenbeam (EB)-ESPRIT, EBMUSIC, and EB-minimum variance distortionless response (EB-MVDR) methods. The directions of arrival (DOAs) of the sound sources and reflective surfaces in an acoustic environment are estimated from the acoustic images of rooms, which are obtained using a microphone array. The applicability of the presented methods in practice is confirmed by experimental results from real measurements. © 2011 EURASI

Comparison of Tracer Gas Methods for Measuring Airflows in Two-Zone Buildings - Heidt, F. D.; Rabenstein, Rudolf (Indoor Air, 1991)
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Comparison of tracer-gas methods for measuring air flows in two-zone buildings - Heidt, F. D.; Rabenstein, Rudolf; Schepers, G. (11th AIVC Conference "Ventilation system performance", 1990)
LMS::1614

Comparison of two different methods for coherent averaging in online ECG analysis - Egorouchkina, K.; Braecklein, M.; Pang, L.; Tchoudovski, I.; Kellermann, Walter; Bolz, Armin (Computers in Cardiology, 2005, 2005)
The coherent averaging is an often used method in ECG systems for noise removing. Most algorithms are developed for the offline data analysis either with automatic or with manual pattern determination for cycles averaging [1-3]. These algorithms can't be used for a wearable long time ECG analysis system [4], which processes the biosignal continuously in real time, because the continuous analysis needs a lot of computational power, which can be provided only by PCs. Therefore, an online averaging algorithm was developed for the ECG signal filtering for wearable long time analysis systems. This article presents an effective algorithm for online ECG complex averaging. Also a comparison of two methods for cycle allocation is offered. © 2005 IEE

Complexity and PSNR-Comparison of Several Fast Motion Estimation Algorithms for MPEG-4 - Kuhn, P.; Diebel, G.; Hermann, S.; Keil, A.; Mooshofer, H.; Kaup, André; Mayer, R.; Stechele, W. (Applications of Digital Image Processing XXI, SPIE, 1998)
LMS::471

Complexity evaluation of random access to coded multi-view video data - Fecker, Ulrich; Kaup, André (15th European Signal Processing Conference, EUSIPCO 2007, 2007)
Significant progress has recently been made in the coding of video data captured with several cameras. Applying image-based rendering algorithms to this data, the user can watch the scene from any desired viewpoint. For that, the renderer may need arbitrary images from different camera views. That is why random access to the coded data is crucial. However, in efficient multi-view coding schemes, random access is complicated by the complex relations between the different frames. In this paper, the Joint Multi-View Video Model (JMVM) is exemplarily used to analyse the complexity of random access to multi-view video sequences. It is shown how the effort to access a certain frame depends on its position in the sequence, and the average and maximum complexity to access a randomly selected frame is quantified. The influence of the number of camera views and the length of each group of pictures is illustrated and discussed. © 2007 EURASI

Complexity Metrics for VVC Decoder Power Reduction in Green Metadata - Herglotz, Christian; Kränzler, Matthias; Dai, Rui; Kaup, André (Picture Coding Symposium, 2024)

Complex-valued frequency selective extrapolation for fast image and video signal extrapolation - Seiler, Jürgen; Kaup, André (IEEE Signal Processing Letters, 2010)
Signal extrapolation tasks arise in miscellaneous manners in the field of image and video signal processing. But, due to the widespread use of low-power and mobile devices, the computational complexity of an algorithm plays a crucial role in selecting an algorithm for a given problem. Within the scope of this contribution, we introduce the complex-valued Frequency Selective Extrapolation for fast image and video signal extrapolation. This algorithm iteratively generates a generic complex-valued model of the signal to be extrapolated as weighted superposition of Fourier basis functions. We further show that this algorithm is up to 10 times faster than the existent real-valued Frequency Selective Extrapolation that takes the real-valued nature of the input signals into account during the model generation. At the same time, the quality which is achievable by the complex-valued model generation is similar to the quality of the real-valued model generation. © 2010 IEE
Keywords: Image and video communication; signal extrapolation

COMPLEX-VALUED SPATIAL AUTOENCODERS FOR MULTICHANNEL SPEECH ENHANCEMENT - Halimeh, Mhd Modar; Kellermann, Walter (2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022)
CRIS-Team WoS Importer:2023-01-13

Component-Wise Power Estimation of Electrical Devices Using Thermal Imaging - Herglotz, Christian; Grosche, Simon; Bharadwaj, Akarsh; Kaup, André (IEEE Transactions on Consumer Electronics, 2021)
This paper presents a novel method to estimate the power consumption of distinct active components on an electronic carrier board by using thermal imaging. The components and the board can be made of heterogeneous material such as plastic, coated microchips, and metal bonds or wires, where a special coating for high emissivity is not required. The thermal images are recorded when the components on the board are dissipating power. In order to enable reliable estimates, a segmentation of the thermal image must be available that can be obtained by manual labeling, object detection methods, or exploiting layout information. Evaluations show that with low-resolution consumer infrared cameras and dissipated powers larger than 300 mW, mean estimation errors of 10% can be achieved.
CRIS-Team WoS Importer:2021-12-31

Compressed domain moving object detection based on H.264/AVC macroblock types - Laumer, Marcus; Amon, P.; Hutter, A.; Kaup, André (8th International Conference on Computer Vision Theory and Applications, VISAPP 2013, 2013)
This paper introduces a low complexity frame-based object detection algorithm for H.264/AVC video streams. The method solely parses and evaluates H.264/AVC macroblock types extracted from the video stream, which requires only partial decoding. Different macroblock types indicate different properties of the video content. This fact is used to segment a scene in fore- and background or, more precisely, to detect moving objects within the scene. The main advantage of this algorithm is that it is most suitable for massively parallel processing, because it is very fast and combinable with several other pre- and post-processing algorithms, without decreasing their performance. The actual algorithm is able to process about 3600 frames per second of video streams in CIF resolution, measured on an Intel® Core™ i5-2520M CPU @ 2.5 GHz with 4 GB RA
Keywords: Compressed domain; H.264/AVC; Macroblock type; Object detection

Compressed domain moving object detection by spatio-temporal analysis of H.264/AVC syntax elements - Laumer, Marcus; Amon, P.; Hutter, Andreas; Kaup, André (31st Picture Coding Symposium, PCS 2015 - with 2015 Packet Video Workshop, PV 2015, 2015)
In this paper, we present a novel moving object detection algorithm for H.264/AVC-compressed video streams. The algorithm does not require full decoding up to the pixel domain but only parsing the compressed bit streams. Thereby, only syntax elements for reconstructing (sub-)macroblock types and quantization parameters are extracted. These features are used to segment the video frames into foreground and background and, according to this segmentation, to identify regions containing moving objects. In a first step, (sub-)macroblock types are analyzed to create initial maps indicating for each block the 'weight' for the presence of a moving object. These maps serve as input for our novel spatio-temporal detection algorithm to refine the weight indicating the level of motion for each block. Finally, quantization parameters of macroblocks are used to apply individual thresholds to the block weights to segment the video frames. Experimental results show that our approach efficiently identifies regions containing moving objects and that the presented algorithm is suitable for processing a large number of video streams in paralle

Compression of 2D and 3D navigation video sequences using skip mode masking of static areas - Springer, Dominic; Simmet, F.; Niederkorn, D.; Kaup, André (29th Picture Coding Symposium, PCS 2012, 2012)
In order to assert correct behavior of electronics in modern automobiles, extensive tests are conducted. Part of these tests focus on the correct rendering of the navigation systems, including map rotation, content of info boxes and smoothness of frame updates. Test engineers have the need to record the rendered navigation system in live setups (e.g. in moving cars) and evaluate them afterwards. Traditional video encoding produces significant bitrate overhead due to the rotating characteristics of the navigation since rotation is approximated with small macroblock partitioning. In this paper, we show how to construct an encoding scheme specifically designed for encoding of 2D and 3D navigation video sequences. For this purpose we develop a Global Motion Estimation (GME) based on feature matching and model parameter estimation and combine it with an H.264/AVC video encoder as backend. By using skip mode information from the H.264/AVC rate distortion optimization, we are able to stabilize the parameter estimation process even in the presence of large static areas. © 2012 IEE

Compression of 2D Navigation Views with Rotational and Translational Motion - Springer, Dominic; Simmet, F.; Niederkorn, D.; Kaup, André (Visual Information Processing and Communication III, 2012)
In-car navigation systems have grown in complexity over the recent years, most notably in terms of route calculation, usability and graphical rendering. In order to guarantee correct system behavior, navigation systems need to be tested under real operating conditions, i.e. with field-tests on the road. In this paper, we will focus on a fast compression solution for 2D navigation renderings, so that field-tests can be archived and handed over to software engineers for subsequent evaluation. No parameters from the rendering procedure are available since access to the system is limited to the raw display signal. Rotation is a dominant factor throughout all navigation sequences, so we show how to reconstruct rotational motion parameters with high accuracy and develop a Global Motion Estimation (GME) method as support for a subsequent H.264/AVC video encoder. By integrating ratedistortion optimization concepts into our scheme, we can efficiently omit the segmentation of static and non-static areas. The runtime of the compression solution, which achieves bitrate savings of up to 19.5%, is evaluated both on a laptop CPU and an embedded OMAP4430 system on chip. © 2012 SPIE-IS&

Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update - Lanz, Daniela; Seiler, Jürgen; Jaskolka, Karina; Kaup, André (Picture Coding Symposium, 2018)
For the lossless compression of dynamic 3-D+t volumes as produced by medical devices like Computed Tomography, various coding schemes can be applied. This paper shows that 3-D subband coding outperforms lossless HEVC coding and additionally provides a scalable representation, which is often required in telemedicine applications. However, the resulting lowpass subband, which shall be used as a downscaled representative of the whole original sequence, contains a lot of ghosting artifacts. This can be alleviated by incorporating motion compensation methods into the subband coder. This results in a high quality lowpass subband but also leads to a lower compression ratio. In order to cope with this, we introduce a new approach for improving the compression efficiency of compensated 3-D wavelet lifting by performing denoising in the update step. We are able to reduce the file size of the lowpass subband by up to 1.64%, while the lowpass subband is still applicable for being used as a downscaled representative of the whole original sequence.<br
Keywords: Computed Tomography; Video Coding; Discrete Wavelet Transform; Motion Compensation; Scalability

Compression of Navigation Video Sequences Using Global Motion Estimation - Springer, Dominic (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2016)
The estimation of global motion in video sequences is difficult, especially when motion between frames is of higher order and strong in magnitude. This thesis sets out to reliably and precisely estimate the motion of the map area in navigation video sequences. Five essential challenges are considered and solved: - Estimation of precise map motion parameters - Estimation of map motion parameters under difficult conditions - Integration strategy into a video codec like HEVC/H.265 - Integration of the overall solution into a larger framework - Fulfillment of given real-time processing requirements As demonstrated, the designed global motion estimation scheme yields significant bitrate savings when integrated into an HEVC/H.265 video encoder. The developed software modules are widely open for configuration, scale adaptively to the available CPU resources, and have been successfully used for a variety of automotive testing scenarios.
Keywords: Datenkompression; Navigation; Bewegungsmessung

Compression of Preprocessed Automotive Radar Data by Using Context-Adaptive Binary Arithmetic Coding - Rückert, Rainer; Li, Yanyan; Herglotz, Christian; Sura, Oliver; Kaup, André; Vossiek, Martin (2024 21st European Radar Conference (EuRAD), 2024)

Compressive online decomposition of dynamic signals via n-l1 minimization with clustered priors - van Luong, Huynh; Deligiannis, Nikos; Forchhammer, Soren; Kaup, André (IEEE Statistical Signal Processing Workshop, 2018)

Compressive Online Robust Principal Component Analysis via n - ℓ1 Minimization - van Luong, Huynh; Deligiannis, Nikos; Seiler, Jürgen; Forchhammer, Soren; Kaup, André (IEEE Transactions on Image Processing, 2018)
This paper considers online robust principal component analysis (RPCA) in time-varying decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Different from conventional batch RPCA, which processes all the data directly, our approach considers a small set of measurements taken per data vector (frame). Moreover, our algorithm can incorporate multiple prior information from previous decomposed vectors via proposing an n - ℓ1 minimization method. At each time instance, the algorithm recovers the sparse vector by solving the n- ℓ1 minimization problem—which promotes not only the sparsity of the vector but also its correlation with multiple previously recovered sparse vectors—and, subsequently, updates the low-rank component using incremental singular value decomposition. We also establish theoretical bounds on the number of measurements required to guarantee successful compressive separation under the assumptions of static or slowly changing low-rank components. We evaluate the proposed algorithm using numerical experiments and online video foreground-background separation experiments. The experimental results show that the proposed method outperforms the existing methods.<br

Compressive Online Robust Principal Component Analysis with Multiple Prior Information - van Luong, Huynh; Deligiannis, Nikos; Seiler, Jürgen; Forchhammer, Soren; Kaup, André (IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017)
Online Robust Principle Component Analysis (RPCA) arises naturally in time-varying signal decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Unlike conventional batch RPCA, which processes all the data directly, our method considers a small set of measurements taken per data vector (frame). Moreover, our method incorporates multiple prior information signals, namely previous reconstructed frames, to improve the separation and thereafter, update the prior information for the next frame. Using experiments on synthetic data, we evaluate the separation performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. The results show that the proposed method outperforms the existing method
Keywords: prior information; robust PCA; compressive measurements; source separation

Compressive Online Robust Principal Component Analysis with Optical Flow for Video Foreground-Background Separation - Prativadibhayankaram, Srivatsa; van Luong, Huynh; Le, Thanh-Ha; Kaup, André (International Symposium on Information and Communication Technology, 2017)
In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and low-rank (background) components. Our method considers a small set of measurements taken per data vector (frame), which is different from conventional batch RPCA, processing all the data directly. The proposed method also incorporates multiple prior information, namely previous foreground and background frames, to improve the separation and then updates the prior information for the next frame. Moreover, the foreground prior frames are improved by estimating motions between the previous foreground frames using optical flow and compensating the motions to achieve higher quality foreground prior. The proposed method is applied to online video foreground and background separation from compressive measurements. The visual and quantitative results show that our method outperforms the existing method

Computational Imaging for Stop-Motion Animated Video Productions - Zilly, F.; Ziegler, M.; Keinert, J.; Schöberl, Michael; Foessel, S. (International Broadcast Convention (IBC) Conference, 2014)
Creating movies using stop-motion technology remains to be a fascinating approach for story telling even in the era of digital cinema. A huge number of professional and semi-professional clips produced as fan-art which can be watched on internet video platforms serve as testimonial for the unbroken interest in unleashing creative potential using this technology. However, producing content using stop-motion remains a cumbersome task, which raises the production costs especially for full length movies. Consequently, a trend can be observed that even for successful television series which were originally produced using stop-motion the production scheme was changed to computer animation. Against this background, we propose within this paper a new production scheme for stop-motion-animated movies which has the potential to lower the production costs while increasing the quality of the resulting content. By using a static multi-camera array and algorithms from the field of computational imaging, our technology allows creating artistic effects in the post-production which are difficult to realize using conventional stop-motion production methods. Our approach allows changing the depth-of-field, smoothly moving the camera along virtual camera paths, and upsampling the frame-rate of the stop-motion-video in high quality. Thereby all effects are computed and applied in post-production, while all intrinsic and extrinsic parameters of the cameras remain static during the whole production. To demonstrate the practicability, within this paper, we show results from a stop motion video which has been produced using the proposed approac
LMS::1443

Computational Imaging for Stop-Motion Animated Video Productions - Zilly, F.; Ziegler, M.; Keinert, J.; Schöberl, Michael; Foessel, S. (The Best of IET and IBC, 2014)
Creating movies using stop-motion technology remains a fascinating method of storytelling, even in the era of digital cinema. The huge number of professional and semi-professional clips produced as fan-art, which can be watched on the internet, serve as testimony to the great interest in unleashing the creative potential of this art-form. However, producing content using stop-motion remains a cumbersome task, with high production costs especially for full-length movies. Consequently, a trend can be observed that even many successful television series which were originally produced using stop-motion, have been moved to computer animation. Against this background, we propose in this paper a new production scheme for stop-motion-animated movies which has the potential to lower production costs while increasing the quality of the resulting content. By using a static multi-camera array and algorithms from the field of computational imaging, our technology permits the creation of artistic effects in post-production which are difficult to realise using conventional stop-motion methods. Our approach allows: changes in the depth-of-field; smooth camera moves along virtual camera paths; and up-sampling o f the frame-rate of the stop-motion-video, all in high quality. All effects are computed and applied in post-production while all intrinsic and extrinsic parameters of the cameras remain fixed during the whole production. To demonstrate the practicability, we shall show with in this paper, results from a stop- motion video which has been produced using the proposed method
LMS::1495
Keywords: cinematography; video signal processing; computer animation

Computationally Efficient Frequency-Domain Combination of Acoustic Echo Cancellation and Robust Adaptive Beamforming - Herbordt, Wolfgang; Buchner, Herbert; Kellermann, Walter (Proc. EUROSPEECH, 2001)
LMS::178

Computationally Efficient Frequency-Domain Robust Generalized Sidelobe Canceller - Herbordt, Wolfgang; Kellermann, Walter (IEEE Workshop on Acoustic Echo and Noise Control (IWAENC), 2001)
LMS::180

Computer Aided Diagnosis of Bone Lesions in the Facial Skeleton - Sörgel, Wolfgang; Girod, S.; Szummer, M.; Girod, Bernd (Proc. Workshop Bildverarbeitung für die Medizin 1998, 1998)
LMS::317

Computer-Assisted Analysis of Hip Joint Flexibility - Teschner, Matthias; Richolt, J.; Kikinis, R.; Girod, Bernd (Proc. of Image and Multidimensiona l Digital Signal Processing IMDSP '98, 1998)
LMS::334

Concept for a CMOS Image Sensor Suited for Analog Image Pre-Processing - Shi, Lan; Söll, Christopher; Bänisch, Andreas; Weigel, Robert; Seiler, Jürgen; Ußmüller, Thomas (DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems, 2015)
A concept for a novel CMOS image sensor suited for analog image pre-processing is presented in this paper. As an example, an image restoration algorithm for reducing image noise is applied as image pre-processing in the analog domain. To supply low-latency data input for analog image preprocessing, the proposed concept for a CMOS image sensor offers a new sensor signal acquisition method in 2D. In comparison to image pre-processing in the digital domain, the proposed analog image pre-processing promises an improved image quality. Furthermore, the image noise at the stage of analog sensor signal acquisition can be used to select the most effective restoration algorithm applied to the analog circuit due to image processing prior to the A/D converte

Conditional emission densities for combining speech enhancement and recognition systems - Sehr, Armin; Yoshioka, Takuya; Delcroix, Marc; Kinoshita, Keisuke; Nakatani, Tomohiro; Maas, Roland; Kellermann, Walter (Annual Conference of the International Speech Communication Association (Interspeech), 2013)
A novel framework based on conditional emission densities for hidden Markov models (HMMs) is proposed in this contribution to integrate speech enhancement systems with automatic speech recognition systems. In the training phase, the observed feature vectors, corrupted by background noise and reverberation, together with estimates for the interference as provided by the speech enhancement system are used for training joint densities of the observations and the interference estimates. In the decoding phase, the joint densities are transformed to conditional densities of the observed features given the interference estimates. Thus, front end processing can be exploited for obtaining interference estimates, and the estimation errors can be modeled very effectively in a data-driven way. Connected digit recognition experiments in a simulated reverberant environment show the potential of the proposed approach: HMMs with the proposed conditional densities outperform various configurations of conventional HMMs in the logarithmic melspectral domain. This is a first step towards using conditional densities for creating synergies between front end and back end. Copyright © 2013 ISC
Keywords: Conditional HMM emission densities; Dereverberation; Frame-by-frame model adaptation; Robust speech recognition; Speech enhancement

Conditional Optimal Filter Selection for Multispectral Object Classification - Kossira, Katja; Schön, David; Seiler, Jürgen; Kaup, André (International Conference on Image Processing (ICIP), 2024)
Capturing images using multispectral camera arrays has gained importance in medical, agricultural and environmental processes. However, using all available spectral bands is infeasible and produces much data, while only a fraction is needed for a given task. Nearby bands may contain similar information, therefore redundant spectral bands should not be considered in the evaluation process to keep complexity and the data load low. In current methods, a restricted and pre-determined number of spectral bands is selected. Our approach improves this procedure by including preset conditions such as noise or the bandwidth of available filters, minimizing spectral redundancy. Furthermore, a minimal filter selection can be conducted, keeping the hardware setup at low costs, while still obtaining all important spectral information. In comparison to the fast binary search filter band selection method, we managed to reduce the amount of misclassified objects of the SMM dataset from 318 to 124 using a random forest classifier.<br
Keywords: Multispectral Imaging; Conditional Optimal Filter Selection; Camera Arrays; Multispectral Classification

Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding - Brand, Fabian; Seiler, Jürgen; Kaup, André (IEEE Transactions on Circuits and Systems For Video Technology, 2024)
Conditional coding is a new video coding paradigm enabled by neural-network-based compression. It can be shown that conditional coding is in theory better than the traditional residual coding, which is widely used in video compression standards like HEVC or VVC. However, on closer inspection, it becomes clear that conditional coders can suffer from information bottlenecks in the prediction path, i.e., that due to the data processing inequality not all information from the prediction signal can be passed to the reconstructed signal, thereby impairing the coder performance. In this paper we propose the conditional residual coding concept, which we derive from information theoretical properties of the conditional coder. This coder significantly reduces the influence of bottlenecks, while maintaining the theoretical performance of the conditional coder. We provide a theoretical analysis of the coding paradigm and demonstrate the performance of the conditional residual coder in a practical example. We show that conditional residual coders alleviate the disadvantages of conditional coders while being able to maintain their advantages over residual coders. In the spectrum of residual and conditional coding, we can therefore consider them as “the best from both worlds”.<br
Keywords: Encoding; Decoding; Channel coding; Video compression; Image reconstruction; Image coding; Standards; Video Compression; Conditional Coding; Conditional Autoencoder; Information Theory

Confidence Estimation and Boosting for Dynamic-Comparator Transient-Noise Analysis - Conrad, Joschua; Kauffman, John; Wilhelmstatter, Simon; Asthana, Rohan; Belagiannis, Vasileios; Ortmanns, Maurits (2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS), 2024)
While designing a comparator, simulation of input-referred noise is crucial. While periodic steady state analyses allow a fast simulation, they are cumbersome to set-up, not compatible with all kinds of circuit models, and require to know the noise-critical operating time within a period. On the contrary, the comparator can be analyzed in a noisy transient simulation with a small test-voltage applied. The noise and offset characteristics can then be derived from a number of simulated comparator decisions and a statistical model. This method is easy to set-up, but has two fundamental simulation parameters: the number of simulated comparator decisions and the magnitude of the applied test-voltage. In this work we derive the statistical confidence into the estimated input-referred noise and show how it depends on the two simulation parameters. It is then shown that this confidence also depends on the true (unknown) input-referred noise. To solve that issue, an algorithm for boosting the convergence is introduced. This algorithm iteratively simulates the comparator decisions and updates the test-voltage for granting a known confidence into the estimated input-referred noise. The algorithm implemented in Verilog-A is available under https://github.com/Joschua-Conrad/Transient-Simulation-of-Dynamic-Comparator-Noise.
CRIS-Team Scopus Importer:2025-02-04
Keywords: Confidence; Dynamic Comparator; Simulation; Transient Noise Analysis

Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation - El-Ghoussani, Amir; Hornauer, Julia; Carneiro, Gustavo; Belagiannis, Vasileios (Third Conference on Lifelong Learning Agents (CoLLAs 2024), 2024)

Content-Adaptive Motion Compensated Frequency Selective Extrapolation for error concealment in video communication - Seiler, Jürgen; Kaup, André (2010 17th IEEE International Conference on Image Processing, ICIP 2010, 2010)
If digital video data is transmitted over unreliable channels such as the internet or wireless terminals, the risk of severe image distortion due to transmission errors is ubiquitous. To cope with this, error concealment can be applied on the distorted data at the receiver. In this contribution we propose a novel spatio-temporal error concealment algorithm, the Content-Adaptive Motion Compensated Frequency Selective Extrapolation. The algorithm operates in two stages, whereas at first the motion in a distorted sequence is estimated. After that, a model of the signal is generated for concealing the distortion. The novel algorithm is based on an already existent error concealment algorithm. But by adapting the model generation to the content of a sequence, the novel algorithm is able to exploit the remaining information, which is still available in the distorted sequence, more effectively compared to the original algorithm. In doing so, a visually noticeable gain of up to 0.51 dB PSNR compared to the underlying algorithm and more than 3 dB compared to other error concealment algorithms can be achieved. © 2010 IEE
Keywords: Error concealment; Spatio-temporal extrapolation; Video communication

Content Adaptive Wavelet Lifting for Scalable Lossless Video Coding - Lanz, Daniela; Herbert, Christian; Kaup, André (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2019)