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1999 | OriginalPaper | Buchkapitel

An Active Contour Model without Edges

verfasst von : Tony Chan, Luminita Vese

Erschienen in: Scale-Space Theories in Computer Vision

Verlag: Springer Berlin Heidelberg

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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 gra- dient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called 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. 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.

Metadaten
Titel
An Active Contour Model without Edges
verfasst von
Tony Chan
Luminita Vese
Copyright-Jahr
1999
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/3-540-48236-9_13