A Variational Framework for Multi-Region Pairwise Similarity-based Image Segmentation

Luca Bertelli, Student Member, IEEE, Baris Sumengen, Member, IEEE,
B. S. Manjunath, Fellow, IEEE, Frederic Gibou


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 1, proposed for background/foreground separation, to the segmentation of images into 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.
[PDF] [BibTex]
Luca Bertelli, Baris Sumengen, B.S. Manjunath and Frederic Gibou,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1400-1414, Aug. 2008.
Node ID: 485 , DB ID: 291 , Lab: VRL , Target: Journal
Subject: [Detection on Images and Videos] « Look up more