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

Documents
00329976.pdf
[img]
[Download]
Request a copy
[img] PDF
00329976.pdf - Whole Document
Restricted to Repository staff only

242kB

Official URL: http://dx.doi.org/10.1049/el:19941275

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.

Item Type:Article
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 By: Tammie Farley
Deposited On:20 Apr 2012 14:01
Last Modified:13 Mar 2013 09:06

Repository Staff Only: item control page