Sparse encoding of binocular images for depth inference
Published in IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2016
Abstract:
Sparse coding models have been widely used to decompose monocular images into linear combinations of small numbers of basis vectors drawn from an overcomplete set. However, little work has examined sparse coding in the context of stereopsis. In this paper, we demonstrate that sparse coding facilitates better depth inference with sparse activations than comparable feed-forward networks of the same size. This is likely due to the noise and redundancy of feed-forward activations, whereas sparse coding utilizes lateral competition to selectively encode image features within a narrow band of depths.
Recommended citation:
SY Lundquist, DM Paiton, PF Schultz and GT Kenyon, “Sparse encoding of binocular images for depth inference,” IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2016, pp. 121-124, doi: 10.1109/SSIAI.2016.7459190.
@INPROCEEDINGS{lundquist2016sparse,
author={Lundquist, Sheng Y. and Paiton, Dylan M. and Schultz, Peter F. and Kenyon, Garrett T.},
booktitle={2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)},
title={Sparse encoding of binocular images for depth inference},
year={2016},
volume={},
number={},
pages={121-124},
doi={10.1109/SSIAI.2016.7459190}
}