title: Independent component analysis applied to feature extraction from colour and stereo images.
abstract: 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 the observed properties of simple cells in the 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.
authors.affiliation: Neural Networks Research Centre, Helsinki University of Technology, Finland. patrik.hoyer@hut.fi
authors.forename: P O
authors.initials: PO
authors.lastname: Hoyer
authors.forename: A
authors.initials: A
authors.lastname: Hyvärinen
mesh_terms: D000465:Algorithms; D003118:Color Perception / Q000502:physiology*; D003867:Depth Perception / Q000502:physiology; D008959:Models, Neurological; D015233:Models, Statistical; D015348:Vision, Binocular / Q000502:physiology*; D014794:Visual Fields / Q000502:physiology