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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| Item Type: | Article |
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| Item Status: | Live Archive |
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. |
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| 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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