Learning Sparse Representations of Depth

  title={Learning Sparse Representations of Depth},
  author={Ivana Tosic and Bruno A. Olshausen and Benjamin J. Culpepper},
  journal={IEEE Journal of Selected Topics in Signal Processing},
This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, such as that obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer… CONTINUE READING
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