TY - JOUR
T1 - Object segmentation using graph cuts based active contours
AU - Xu, Ning
AU - Bansal, Ravi
AU - Ahuja, Narendra
N1 - Funding Information:
This work is partly supported by National Science Foundation under Grant ECS-0225523, and is partly completed at Siemens Corporate Research, Inc. Their support is gratefully acknowledged.
PY - 2003
Y1 - 2003
N2 - In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. Consequently, it has the following advantages. (1). It has the ability to jump over local minima and provide a more global result. (2). Graph cuts guarantee continuity and lead to smooth contours free of self-crossing and uneven spacing problems. Therefore, the internal force which is commonly used in traditional energy functions to control the smoothness is no longer needed, and hence the number of parameters is greatly reduced. (3). Our approach easily extends to the segmentation of three and higher dimensional objects. In addition, the algorithm is suitable for interactive correction and is shown to always converge. Experimental results and analyses are provided.
AB - In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization tool of graph cuts and differs fundamentally from traditional active contours in that it uses graph cuts to iteratively deform the contour. Consequently, it has the following advantages. (1). It has the ability to jump over local minima and provide a more global result. (2). Graph cuts guarantee continuity and lead to smooth contours free of self-crossing and uneven spacing problems. Therefore, the internal force which is commonly used in traditional energy functions to control the smoothness is no longer needed, and hence the number of parameters is greatly reduced. (3). Our approach easily extends to the segmentation of three and higher dimensional objects. In addition, the algorithm is suitable for interactive correction and is shown to always converge. Experimental results and analyses are provided.
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M3 - Conference article
AN - SCOPUS:0042442019
SN - 1063-6919
VL - 2
SP - II/46-II/53
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
T2 - 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003
Y2 - 18 June 2003 through 20 June 2003
ER -