Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Detection and correction of disparity estimation errors via supervised learning

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

3 Author(s)
Varekamp, C. ; TP-Vision Eindhoven, Eindhoven, Netherlands ; Hinnen, K. ; Simons, W.

We propose a supervised learning method for detecting disparity estimation errors in a disparity map. A classifier is trained using features of low computational complexity. The proposed method can in principle be used to improve the performance of any disparity estimation algorithm. The results presented in this paper are therefore of general interest for those working on disparity estimation. In addition, our method solves the problem of needing a large variation of input stereo video with ground truth disparity. In our approach, we visually inspect a disparity map and manually annotate blocks that appear to be errors and blocks that appear to be correct. We then train a classifier to do this work automatically. Recursive predictions are used to correct errors. Our manual annotation approach has the advantage that `ground truth' data is generated via low-cost annotation of arbitrary stereo video.

Published in:

3D Imaging (IC3D), 2013 International Conference on

Date of Conference:

3-5 Dec. 2013