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Active contours without edges
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OA
AI
DOI：10.1109/83.902291.png)
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Abstract
En 中文
In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can he 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, hut is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will 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.
Keywords:
active contours
curvature
energy minimization
finite differences
level sets
partial differential equations
segmentation
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
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