Active contours using a constraint-based implicit representation.

Morse BS, Liu W, Yoo TS, Subramanian KR

Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR); 2005 Jun 20-25; San Diego. 285-92. DOI: 10.1109/CVPR.2005.59.

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Abstract:

We present a new constraint-based implicit active con- tour, which shares desirable properties of both parametric and implicit active contours. Like parametric approaches, their representation is compact and can be manipulated in- teractively. Like other implicit approaches, they can natu- rally adapt to non-simple topologies. Unlike implicit approaches using level-set methods, rep- resentation of the contour does not require a dense mesh. Instead, it is based on specified on-curve and off-curve con- straints, which are interpolated using radial basis functions. These constraints are evolved according to specified forces drawn from the relevant literature of both parametric and implicit approaches. This new type of active contour is demonstrated through synthetic images, photographs, and medical images with both simple and non-simple topologies. For complex in- put, this approach produces results comparable to those of level set or parameterized finite-element active models, but with a compact analytic representation. As with other ac- tive contours they can also be used for tracking, especially for multiple objects that split or merge.

Morse BS, Liu W, Yoo TS, Subramanian KR. Active contours using a constraint-based implicit representation. 
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR); 2005 Jun 20-25; San Diego. 285-92. DOI: 10.1109/CVPR.2005.59.