By Topic

Unsupervised texture image segmentation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Mocofan, M. ; Politehnic Inst., Timisoara, Romania ; Caleanu, C. ; Lacrama, D. ; Alexa, F.

This paper is focused on a hierarchical structure of modular self-organizing neural networks for unsupervised texture segmentation (sofm-nn). Input data consists of local information regarding textures (cooccurrence matrix elements) and the texture image itself. An unsupervised segmentation is done using a sofm-nn network and then the final segmentation is performed by another sofm-nn neural network using the previously obtained results. Experimental results show the efficiency of the proposed method

Published in:

Neural Network Applications in Electrical Engineering, 2000. NEUREL 2000. Proceedings of the 5th Seminar on

Date of Conference: