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Feature extraction from colour and stereo images using ICA

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2 Author(s)
Hoyer, P.O. ; Neural Networks Res. Centre, Helsinki Univ. of Technol., Espoo, Finland ; Hyvarinen, A.

Previous work has shown that independent component analysis (ICA) applied to 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, compression, and pattern recognition

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Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:3 )

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