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

Statistical Model of Similarity Transformations: Building a Multi-Object Pose

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)
Bossa, M.N. ; University of Zaragoza, Spain ; Olmos, S.

In most of computational anatomy studies, pose is disregarded because pose information mainly depends on non relevant external factors. However, the relative pose among different objects belonging to a complex multi-object system may be useful for diagnosis, prognosis and monitoring. In this work a methodology to build statistical multi-object pose models (MOPM) is described. The methodology is based on Principal Geodesic Analysis because the space of similarity transformations does not form a vector space. Methods to compute statistics, namely averages and variation modes, are described in detail. Experimental results are performed on neuroanatomical structures such as the subcortical nuclei (caudate nucleus, hippocampus, amygdala, thalamus, putamen, pallidum) and lateral ventricles. We expect that multi-object pose models will be useful as a valuable a priori information about relative location, orientation and scale of each structure. This compact model will be relevant as a coarse initialization for segmentation, or regularization of segmentation and registration algorithms.

Published in:

Computer Vision and Pattern Recognition Workshop, 2006. CVPRW '06. Conference on

Date of Conference:

17-22 June 2006

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.