Unsupervised segmentation of textured images using a hierarchical neural structure

Yin, H. and Allinson, N. M. (1994) Unsupervised segmentation of textured images using a hierarchical neural structure. Electronics letters, 30 (22). pp. 1842-1843. ISSN 0013-5194

Full content URL: http://dx.doi.org/10.1049/el:19941275

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Abstract

A hierarchical learning structure, combining a randomly-placed local window, a self-organising map and a local-voting scheme, has been developed for the unsupervised segmentation of textured images, which are modelled by Markov random fields. The system learns to progressively estimate model parameters, and hence classify the various textured regions. A globally correct segregation has consistently been obtained during extensive experiments on both synthetic and natural textured images.

Additional Information:A hierarchical learning structure, combining a randomly-placed local window, a self-organising map and a local-voting scheme, has been developed for the unsupervised segmentation of textured images, which are modelled by Markov random fields. The system learns to progressively estimate model parameters, and hence classify the various textured regions. A globally correct segregation has consistently been obtained during extensive experiments on both synthetic and natural textured images.
Keywords:neural networks, segmentation
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:5074
Deposited On:20 Apr 2012 14:01

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