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

Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint

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

4 Author(s)
Willert, V. ; HRI Eur. GmbH, Offenbach ; Eggert, J. ; Toussaint, M. ; Korner, E.

The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constraint in a probabilistic way assuming Gaussian distributed uncertainty in spatial identification of velocity measurements and extend this idea to scale and time dimensions. Thus, we are able to combine uncertain velocity measurements observed at different image scales and positions over time. We arrive at a new recurrent optical flow filter formulated in a dynamic Bayesian network applying suitable factorisation assumptions and approximate inference techniques. The introduction of spatial uncertainty allows for a dynamic and spatially adaptive tuning of the constraining neighborhood. Here, we realize this tuning dependent on the local structure tensor of the intensity patterns of the image sequence. We demonstrate that a probabilistic combination of spatiotemporal integration and modulation of a purely local integration area improves the Lucas and Kanade estimation.

Published in:

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Date of Conference:

11-13 Dec. 2008

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.