Geodesic active regions and level set methods for supervised texture segmentation

被引:557
作者
Paragios, N [1 ]
Deriche, R
机构
[1] Siemens Corp Res, Imaging & Visualizat Dept, Princeton, NJ 08540 USA
[2] INRIA, Comp Vis & Robot Grp, F-06902 Sophia Antipolis, France
关键词
supervised texture segmentation; Gabor filters; mixture analysis; Geodesic Active Contours; propagation of curves; level set methods;
D O I
10.1023/A:1014080923068
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonstrate the potentials of the proposed framework. The textured feature space is generated by filtering the given textured images using isotropic and anisotropic filters, and analyzing their responses as multi-component conditional probability density functions. The texture segmentation is obtained by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution is guided by boundary and region-based segmentation forces, and is constrained by a regularity force. The level set implementation is performed using a fast front propagation algorithm where topological changes are naturally handled. The performance of our method is demonstrated on a variety of synthetic and real textured frames.
引用
收藏
页码:223 / 247
页数:25
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