Graph partitioning active contours (GPAC) for image segmentation.

Baris Sumengen; B S Manjunath

doi:10.1109/tpami.2006.76

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Graph partitioning active contours (GPAC) for image segmentation.

Baris Sumengen·B S Manjunath

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Abstract

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.

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Algorithms Artificial Intelligence Computer Graphics Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Signal Processing, Computer-Assisted Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Baris Sumengen, B S Manjunath. 2006. Graph partitioning active contours (GPAC) for image segmentation.. https://doi.org/10.1109/tpami.2006.76

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