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

DIRSAC: A directed sampling and consensus approach to quasi-degenerate data fitting

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)
Baker, C.L. ; Nat. Robot. Eng. Center, Pittsburgh, PA, USA ; Hoff, W.

In this paper we propose a new data fitting method which, similar to RANSAC, fits data to a model using sample and consensus. The application of interest is fitting 3D point clouds to a prior geometric model. Where the RANSAC process uses random samples of points in the fitting trials, we propose a novel method which directs the sampling by ordering the points according to their contribution to the solution's constraints. This is particularly important when the data is quasi-degenerate. In this case, the standard RANSAC algorithm often fails to find the correct solution. Our approach selects points based on a Mutual Information criterion, which allows us to avoid redundant points that result in degenerate sample sets. We demonstrate our approach on simulated and real data and show that in the case of quasi-degenerate data, the proposed algorithm significantly outperforms RANSAC.

Published in:

Applications of Computer Vision (WACV), 2013 IEEE Workshop on

Date of Conference:

15-17 Jan. 2013

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.