By Topic

A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation

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)
Bertelli, L. ; Electr. & Comput. Eng. Dept., Univ. of California, Santa Barbara, Santa Barbara, CA ; Sumengen, B. ; Manjunath, B.S. ; Gibou, F.

Variational cost functions that are based on pairwise similarity between pixels can be minimized within level set framework resulting in a binary image segmentation. In this paper we extend such cost functions and address multi-region image segmentation problem by employing a multi-phase level set framework. For multi-modal images cost functions become more complicated and relatively difficult to minimize. We extend our previous work, proposed for background/foreground separation, to the segmentation of images in more than two regions. We also demonstrate an efficient implementation of the curve evolution, which reduces the computational time significantly. Finally, we validate the proposed method on the Berkeley segmentation data set by comparing its performance with other segmentation techniques.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:30 ,  Issue: 8 )