Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging

Genser N, Spruck A, Seiler J, Kaup A (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Event location: Abu Dhabi

DOI: 10.1109/icip40778.2020.9191353

Abstract

Recently, cross-spectral stereo-camera setups found their way from special applications to mass market, especially in smartphones, automotive systems, or drones. In the following, a novel concept is introduced to bring stereo cameras and cross-spectral disparity estimation together. So far, either monomodal stereo algorithms exist that are not suitable for cross-spectral image registration, or structural template matching is applied that achieves a low quality. To overcome these limitations, a technique is proposed to synthesize arbitrary spectral components from widely available color stereo databases, and to retrain mono-modal deep learning methods. In this contribution, the estimation of spectral bands based on random processes is shown together with noise models, which also allow for a robust registration of narrowband components. The theoretical examination is completed by an extensive evaluation, including a self-manufactured cross-spectral camera setup. In comparison to state-of-the-art techniques, the end-point error is on average reduced by a factor of seven.

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How to cite

APA:

Genser, N., Spruck, A., Seiler, J., & Kaup, A. (2020). Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging. In Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP). Abu Dhabi, AE.

MLA:

Genser, Nils, et al. "Deep Learning Based Cross-Spectral Disparity Estimation For Stereo Imaging." Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi 2020.

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