An adaptive computational model for texture segmentation

Author

Caelli, Terry M.

Author_Institution

Inst. fur Med. Psychol., Munich, West Germany

Volume

18

Issue

1

fYear

1988

Firstpage

9

Lastpage

17

Abstract

Extensions to current models for texture segmentation are presented, in which the underlying detector (filter) mechanisms are allowed to adapt to the incoming signal in terms of their dynamical response range and associativities. This system converges on new texton (B. Julesz, 1981) profiles of minimal dimensionality that are used to classify texture regions by a minimum distance classifier in the texture feature space. The three processes of convolution, cooperativity, and classification are individually analyzed and compared with some observations from human texture discrimination experiments

Keywords

filtering and prediction theory; pattern recognition; adaptive computational model; adaptive filtering; classification; convolution; cooperativity; human texture discrimination; minimal dimensionality; minimum distance classifier; pattern recognition; texton; texture segmentation; Adaptive filters; Adaptive signal detection; Computational modeling; Convolution; Detectors; Humans; Image segmentation; Intelligent robots; Psychology; Visual system;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.87051

Filename

87051