Cart (Loading....) | Create Account
Close category search window

An image representation algorithm compatible with neural-associative-processor-based hardware recognition systems

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

2 Author(s)
Yagi, M. ; Dept. of Electron. Eng., Tokyo Univ., Japan ; Shibata, T.

A robust image representation algorithm compatible with the VLSI-matching-engine-based image recognition system has been developed. The spatial distributions of four-principal-direction edges in a 64 × 64-pels gray scale image are coded to form a 64-dimension feature vector. Since the 2D edge information is reduced to a feature vector by projecting edge flags to the principal directions, it is named the projected principal-edge distribution (PPED) representation. The PPED vectors very well preserve the human perception of similarity among images in the vector space, while achieving a substantial dimensionality reduction in the image data. The PPED algorithm has been applied to medical radiograph analysis, which was taken as a test vehicle for algorithm optimization. The robust nature of the PPED representation has been confirmed by the recognition results comparable to the diagnosis by experts having several years of experience in a university hospital. Dedicated digital VLSI circuits have been developed for PPED vector generation in order to expedite the processing. A test hardware recognition system was constructed using the vector generation circuits, where the analog neural associative processor chip developed in a separate project was employed as a vector-matching engine. As a result, a successful medical radiograph analysis has been experimentally demonstrated using the hardware system. Feasibility of a very low-power operation of the system has been also demonstrated.

Published in:

Neural Networks, IEEE Transactions on  (Volume:14 ,  Issue: 5 )

Date of Publication:

Sept. 2003

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.