Object segmentation using graph cuts based active contours

Authors:
Ning Xu;Narendra Ahuja;Ravi Bansal
Affiliations:
DMS Lab, Samsung Information Systems America, 3345 Michelson Dr., Suite 250, Irvine, CA 92612, USA;ECE Department, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;Department of Psychiatry, Columbia University, New York, NY, USA
Venue:
Computer Vision and Image Understanding
Year:
2007

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Abstract

In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation. GCBAC approach is a combination of the iterative deformation idea of active contours and the optimization tool of graph cuts. It differs from traditional active contours in that it uses graph cuts to iteratively deform the contour and its cost function is defined as the summation of edge weights on the cut. The resulting contour at each iteration is the global optimum within a contour neighborhood (CN) of the previous result. Since this iterative algorithm is shown to converge, the final contour is the global optimum within its own CN. The use of contour neighborhood alleviates the well-known bias of the minimum cut in favor of a shorter boundary. GCBAC approach easily extends to the segmentation of three and higher dimensional objects, and is suitable for interactive correction. Experimental results on selected data sets and performance analysis are provided.