Shape Prior Segmentation of Multiple Objects with Graph Cuts

Nhat Vu and B.S. Manjunath
Department of Electrical and Computer Engineering
University of California, Santa Barbara, CA 93106-9560
{nhat,manj} [at]


We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.
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Nhat Vu and B.S. Manjunath,
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, Jun. 2008.
Node ID: 496 , DB ID: 303 , Lab: VRL , Target: Conference
Subject: [Detection on Images and Videos] « Look up more