Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Hopfield neural network (hnn) improvement for color image recognition using multi-bitplane and multi-connect architecture

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
$31 $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

1 Author(s)
Mutter, K.N. ; Sch. of Phys., Univ. Sains Malaysia, Penang

A new approach of using HNN with multi-connect architecture in color image recognition has been produced in this work. HNN consists of a single layer of fully connected processing elements, which is described as an associative memory. However, HNN is useless in dealing with data not in bipolar representation. As such, HNN failed to work directly with color images, unless, another way is produced in order to pave the way for expected right recognition. In RGB bands each represents different values of brightness, still it is possible to assume for 8-bit RGB image consists of 8-layers of binaries, or bipolar. In such way, each layer is as a single binary image for HNN. The results have shown the possibility and usefulness of HNN in RGB image recognition. Besides, the possibility of using wide number of RGB images stored in the net memory without sensed affection on the final results.

Published in:

Computer Graphics, Imaging and Visualisation, 2007. CGIV '07

Date of Conference:

14-17 Aug. 2007