Title
Active contours without edges.
Abstract
We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a “mean-curvature flow”-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected
Year
DOI
Venue
2001
10.1109/83.902291
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
Field
DocType
active contours,experimental results,minimal partition problem,curve evolution,functional equations,image segmentation,mumford-shah functional,finite differences,numerical algorithm,initial curve,mean-curvature flow,object detection,stopping term,finite difference methods,level sets,active contour,mean curvature flow,level set,mathematics,partial differential equations,helium
Mumford–Shah functional,Active contour model,Object detection,Pattern recognition,Level set method,Signed distance function,Image processing,Level set,Image segmentation,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
10
2
1057-7149
Citations 
PageRank 
References 
3477
166.75
15
Authors
2
Search Limit
1001000
Name
Order
Citations
PageRank
Tony F. Chan18733659.77
Luminita A. Vese25389302.64