By Topic

Adaptive hyperplane algorithm for texture characterization

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

2 Author(s)
E. K. Hajeer ; Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA ; I. K. Sethi

Textural features are often consider as one of the most powerful features in describing the intrinsic physical properties of object surfaces in a scene. In this paper, we propose characterizing image textures by a least-squares hyperplane fitting of their image multidimensional primitives. The adaptive implementation of the hyperplane fitting process is carried out by a newly proposed nonlinear supervised neural unit trained by a constrained form of anti-Hebbian learning. Experimental results are presented to demonstrate the performance of the proposed texture characterization model in image classification and segmentation applications

Published in:

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on  (Volume:3 )

Date of Conference:

2-5 Oct 1994