Paper Info
Title
An alternating split Bregman algorithm for multi-region segmentation.
Abstract
Multi-region image segmentation aims at partitioning an image into several "meaningful" regions. The associated optimization problem is non-convex and generally difficult to solve. Finding the global optimum, or good approximations of it, hence is a problem of first interest in computer vision. We propose an alternating split Bregman algorithm for a large class of convex relaxations of the continuous Potts segmentation model. We compare the algorithm to the primal-dual approach and show examples from the Berkeley image database and from live-cell fluorescence microscopy.
Year
DOI
Venue
2011
10.1109/ACSSC.2011.6190034
ACSCC
Keywords
DocType
ISSN
computer vision,concave programming,image segmentation,iterative methods,Berkeley image database,alternating split Bregman algorithm,computer vision,continuous Potts segmentation model,convex relaxations,live-cell fluorescence microscopy,multiregion image segmentation,nonconvex optimization problem,primal-dual approach
Conference
1058-6393
Citations 
PageRank 
References 
2
0.37
0
Authors
3
Name
Order
Citations
PageRank
Grégory Paul1483.84
Janick Cardinale2281.67
Ivo F. Sbalzarini318718.80