title: Image segmentation using a sparse coding model of cortical area V1.
abstract: Algorithms that encode images using a sparse set of basis functions have previously been shown to explain aspects of the physiology of a primary visual cortex (V1), and have been used for applications, such as image compression, restoration, and classification. Here, a sparse coding algorithm, that has previously been used to account for the response properties of orientation tuned cells in primary visual cortex, is applied to the task of perceptually salient boundary detection. The proposed algorithm is currently limited to using only intensity information at a single scale. However, it is shown to out-perform the current state-of-the-art image segmentation method (Pb) when this method is also restricted to using the same information.
authors.affiliation: Department of Informatics, King’s College London, London, UK. michael.spratling@kcl.ac.uk
authors.forename: Michael W
authors.initials: MW
authors.lastname: Spratling
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology