Previous work has shown that independent component analysis (ICA) applied to feature extraction from natural image data yields features resembling Gabor functions and simple-cell receptive fields. This article considers the effects of including chromatic and stereo information. The inclusion of colour leads to features divided into separate red/green, blue/yellow, and bright/dark channels. Stereo image data, on the other hand, leads to binocular receptive fields which are tuned to various disparities. The similarities between these results and observed properties of simple cells in primary visual cortex are further evidence for the hypothesis that visual cortical neurons perform some type of redundancy reduction, which was one of the original motivations for ICA in the first place. In addition, ICA provides a principled method for feature extraction from colour and stereo images; such features could be used in image processing operations such as denoising and compression, as well as in pattern recognition. Short title: ICA features of colour and stereo images Acknowledgments: The authors would like to thank Jukka Häkkinen, Jari Laarni, Harri Lappalainen, Pentti Laurinen, Bruno Olshausen, and Tarja Peromaa for stimulating and useful discussions. In addition, we wish to thank the anonymous reviewers for suggestions which helped improve the clarity of this paper.
Hoyer and Hyvärinen REF have applied the independent component analysis (ICA) to learn the orthogonal basis of stereo images.
Independent component analysis applied to feature extraction from colour and stereo images
COMPUTATION IN NEURAL SYSTEMS
Computer Science Medicine