Abstract | ||
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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 |
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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 |
Name | Order | Citations | PageRank |
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Tony F. Chan | 1 | 8733 | 659.77 |
Luminita A. Vese | 2 | 5389 | 302.64 |