Geodesic active contours
Geodesic active contours
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DOI：
10.1023/a:1007979827043
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发表时间：
1997-02-01
影响因子：
19.5
通讯作者：
Sapiro, G
中科院分区：
文献类型：
--
作者：
Caselles, V;Kimmel, R;Sapiro, G
提出了一种新的目标边界检测方法。该技术是基于动态轮廓演变的时间根据内在的几何措施的图像。不断变化的轮廓自然地分裂和合并，允许同时检测多个对象以及内部和外部边界。该方法是基于活动轮廓和测地线或最小距离曲线的计算之间的关系。最小距离曲线位于黎曼空间中，其度量由图像内容定义。这种用于对象分割的测地线方法允许连接基于能量最小化的经典“蛇”和基于曲线演化理论的几何活动轮廓。改进了以往的几何活动轮廓模型，使其在梯度变化较大（包括间隙）的情况下能够稳定地检测边界。关于演化的存在性，唯一性，稳定性和正确性的正式结果，以及。该方案采用了一种高效的曲线演化算法。实验结果表明，该计划应用于真实的图像，包括物体与孔和医疗数据图像证明了它的权力。结果也可以扩展到3D对象分割。
A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric is defined by the image content. This geodesic approach for object segmentation allows to connect classical ''snakes'' based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well. The scheme was implemented using an efficient algorithm for curve evolution. Experimental results of applying the scheme to real images including objects with holes and medical data imagery demonstrate its power. The results may be extended to 3D object segmentation as well.