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S. Edeleman
ABSTRACT
Visual categorization, or making sense of novel shapes and shape classes, is a computationally challenging and behaviorally important task, which is not widely addressed in computer vision or visual psychophysics (where the stress is rather on the generalization of recognition across changes of viewpoint). This paper examines the categorization abilities of four current approaches to object representation: structural descriptions, geometric models, multidimensional feature spaces, and similarities to reference shapes. It is proposed that a scheme combining features of all four approaches is a promising candidate for a comprehensive and computationally feasible theory of categorization. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Computational theories of object recognitionSite generated on Friday, 06 January 2006