By Topic

A Bayesian Theoretic Approach to Multiscale Complex-Phase-Order Representations

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

1 Author(s)
Alexander Wong ; University of Waterloo, Waterloo, Canada

This paper explores a Bayesian theoretic approach to constructing multiscale complex-phase-order representations. We formulate the construction of complex-phase-order representations at different structural scales based on the scale-space theory. Linear and nonlinear deterministic approaches are explored, and a Bayesian theoretic approach is introduced for constructing representations in such a way that strong structure localization and noise resilience are achieved. Experiments illustrate its potential for constructing robust multiscale complex-phase-order representations with well-localized structures across all scales under high-noise situations. Illustrative examples of applications of the proposed approach is presented in the form of multimodal image registration and feature extraction.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 1 )