Title
Region-Based Image Segmentation Using Texture Statistics And Level-Set Methods
Abstract
We propose a novel multi-class method for texture segmentation. The segmentation issue is stated as the minimization of a region-based functional that involves a weighted Kullback-Leibler measure between distributions of local texture features and a regularization term that imposes smoothness and regularity of region boundaries. The proposed approach is implemented using level-set methods, and partial differential equations (PDE) are expressed using shape derivative tools introduced in S. Jehan-Besson et al. (2003). As an application, we have tested the method using cooccurrence distributions to segment synthetic mosaics of textures from the Brodatz album, as well as real textured sonar images. These results prove the relevance of the proposed approach for supervised and unsupervised texture segmentation
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1660437
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference
Keywords
Field
DocType
image segmentation,image texture,partial differential equations,statistics,Brodatz album,PDE,level-set methods,partial differential equations,region-based image segmentation,segment synthetic mosaics,texture segmentation,texture statistics,weighted Kullback-Leibler measure
Computer vision,Histogram,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Image texture,Level set,Image segmentation,Regularization (mathematics),Artificial intelligence,Region growing
Conference
Volume
ISSN
ISBN
2
1520-6149
1-4244-0469-X
Citations 
PageRank 
References 
8
0.58
6
Authors
4
Name
Order
Citations
PageRank
Imen Karoui1232.06
Ronan Fablet231247.04
Jean-Marc Boucher380.58
Jean-Marie Augustin480.58