Published In
IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)
Document Type
Article
Publication Date
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.
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DOI
10.1109/SSIAI.2016.7459190
Persistent Identifier
http://archives.pdx.edu/ds/psu/21110
Citation Details
S. Y. Lundquist, D. M. Paiton, P. F. Schultz and G. T. Kenyon, "Sparse encoding of binocular images for depth inference," 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, 2016, pp. 121-124.
Description
To the best of our knowledge, this work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105.