UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Natural image profiles are most likely to be step edges

Griffin, LD; Lillholm, M; Nielsen, M; (2004) Natural image profiles are most likely to be step edges. Vision Research , 44 (4) 407 - 421. 10.1016/j.visres.2003.09.025.

Full text not available from this repository.

Abstract

We introduce Geometric Texton Theory (GTT), a theory of categorical visual feature classification that arises through consideration of the metamerism that affects families of co-localised linear receptive-field operators. A refinement of GTT that uses maximum likelihood (ML) to resolve this metamerism is presented. We describe a method for discovering the ML element of a metamery class by analysing a database of natural images. We apply the method to the simplest case––the ML element of a canonical metamery class defined by co-registering the location and orientation of profiles from images, and affinely scaling their intensities so that they have identical responses to 1-D, zeroth- and first-order, derivative of Gaussian operators. We find that a step edge is the ML profile. This result is consistent with our proposed theory of feature classification.

Type:Article
Title:Natural image profiles are most likely to be step edges
DOI:10.1016/j.visres.2003.09.025
Publisher version:http://dx.doi.org/10.1016/j.visres.2003.09.025
Language:English
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

Archive Staff Only: edit this record