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A new graph cut-based multiple active contour algorithm without initial contours and seed points

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Abstract

This paper presents a new graph cut-based multiple active contour algorithm to detect optimal boundaries and regions in images without initial contours and seed points. The task of multiple active contours is framed as a partitioning problem by assuming that image data are generated from a finite mixture model with unknown number of components. Then, the partitioning problem is solved within a divisive graph cut framework where multi-way minimum cuts for multiple contours are efficiently computed in a top-down way through a swap move of binary labels. A split move is integrated into the swap move within that framework to estimate the model parameters associated with regions without the use of initial contours and seed points. The number of regions is also estimated as a part of the algorithm. Experimental results of boundary and region detection of natural images are presented and analyzed with precision and recall measures to demonstrate the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. Division of Electrical and Computer Engineering, POSTECH, San 31 Hyoja-Dong, Pohang, 790-784, South Korea

    Jong-Sung Kim & Ki-Sang Hong

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  1. Jong-Sung Kim
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  2. Ki-Sang Hong
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Correspondence to Jong-Sung Kim.

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Kim, JS., Hong, KS. A new graph cut-based multiple active contour algorithm without initial contours and seed points. Machine Vision and Applications 19, 181–193 (2008). https://doi.org/10.1007/s00138-007-0090-2

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  • DOI: https://doi.org/10.1007/s00138-007-0090-2

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