Active contours without edges
Active contours without edges
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DOI：
10.1109/83.902291
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发表时间：
2001-02-01
影响因子：
10.6
通讯作者：
Vese, LA
中科院分区：
文献类型：
--
作者：
Chan, TF;Vese, LA
在本文中，我们提出了一个新的模型，主动轮廓检测目标在一个给定的图像，基于曲线演化技术，Mumford-Shah功能的分割和水平集。我们的模型可以检测边界不一定由梯度定义的对象。我们最小化能量，这可以看作是最小划分问题的一个特殊情况，在水平集公式中，问题变成了一个“平均曲率流”，类似于活动轮廓的演化，它将停止在期望的边界上。然而，停止项不依赖于的梯度。图像，如在经典的活动轮廓模型中，而是与图像的特定分割相关。我们将给出一个使用有限差分的数值算法。最后，我们将提出各种实验结果，特别是一些经典的蛇方法的基础上的梯度是不适用的例子。此外，初始曲线可以在图像中的任何位置，并且自动检测内部轮廓。
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.