Paul J, Stechele W, Kröhnert M, Asfour T, Oechslein B, Erhardt C, Schedel J, Lohmann D, Schröder-Preikschat W (2014)
Publication Type: Conference contribution
Publication year: 2014
Publisher: Springer
Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series: Lecture Notes in Computer Science
City/Town: Basel
Book Volume: 8350
Pages Range: 1-12
Conference Proceedings Title: Architecture of Computing Systems ARCS 2014
Event location: Lübeck, Germany
ISBN: 978-3-319-04890-1
DOI: 10.1007/978-3-319-04891-8
Corner-detection techniques are being widely used in computer vision - for example in object recognition to find suitable candidate points for feature registration and matching. Most computer-vision applications have to operate on real-time video sequences, hence maintaining a consistent throughput and high accuracy are important constrains that ensure high-quality object recognition. A high throughput can be achieved by exploiting the inherent parallelism within the algorithm on massively parallel architectures like many-core processors. However, accelerating such algorithms on many-core CPUs offers several challenges as the achieved speedup depends on the instantaneous load on the processing elements. In this work, we present a new resource-aware Harris corner-detection algorithm for many-core processors. The novel algorithm can adapt itself to the dynamically varying load on a many-core processor to process the frame within a predefined time interval. The results show a 19% improvement in throughput and an 18% improvement in accuracy. © 2014 Springer International Publishing Switzerland.
APA:
Paul, J., Stechele, W., Kröhnert, M., Asfour, T., Oechslein, B., Erhardt, C.,... Schröder-Preikschat, W. (2014). Resource-Aware Harris Corner Detection Based on Adaptive Pruning. In Architecture of Computing Systems ARCS 2014 (pp. 1-12). Lübeck, Germany, DE: Basel: Springer.
MLA:
Paul, Johny, et al. "Resource-Aware Harris Corner Detection Based on Adaptive Pruning." Proceedings of the ARCS 2014, Lübeck, Germany Basel: Springer, 2014. 1-12.
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