By Topic

Computer Vision Studies Using Stochastic Resonance/Information-theoretic Methods

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

3 Author(s)
D. W. Repperger ; Members, IEEE, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433 USA. 937-255-8765; fax: 937-255-8752; e-mail: ; R. G. Roberts ; A. R. Pinkus

An investigation into computer vision techniques is conducted using a procedure from nonlinear dynamics termed "stochastic resonance." This work involves concepts from detection theory, information theory and nonlinear dynamics. An information distance metric is synthesized which helps define the dependent measure to be used with the stochastic resonance optimization. Monte Carlo simulations show the efficacy of the proposed method. A class of test objects are presented to fairly evaluate the utility of the proposed methods introduced.

Published in:

2007 International Symposium on Computational Intelligence in Robotics and Automation

Date of Conference:

20-23 June 2007