### Abstract

Active contours is a popular technique for image segmentation. However, active contour tend to converge to the closest local minimum of its energy function and often requires a close boundary initialization. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the active contour as a mean curve of the probability density function p(x). It moves to minimize the Kullback-Leibler (KL) divergence between p(x) and the probability density function derived from the image. KL divergence forces p(x) to .cover all image areas. and the uncovered areas are heavily penalized, which allows the active contour to go over the edges. Also we use deterministic annealing on the width of p(x) to implement a coarse-to-fine search strategy. In the limit, when the width of p(x) goes to zero, the KL divergence function converges to the conventional external energy term (which can be seen a special case) of active contours. Our method produces robust segmentation results from arbitrary initialization positions.

Original language | English (US) |
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Title of host publication | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 |

Pages | 2798-2804 |

Number of pages | 7 |

DOIs | |

State | Published - 2009 |

Event | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 - Miami, FL, United States Duration: Jun 20 2009 → Jun 25 2009 |

### Other

Other | 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 |
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Country | United States |

City | Miami, FL |

Period | 6/20/09 → 6/25/09 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Vision and Pattern Recognition
- Biomedical Engineering

### Cite this

*2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009*(pp. 2798-2804). [5206552] https://doi.org/10.1109/CVPRW.2009.5206552

**Global active contour-based image segmentation via probability alignment.** / Myronenko, Andriy; Song, Xubo.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009.*, 5206552, pp. 2798-2804, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, Miami, FL, United States, 6/20/09. https://doi.org/10.1109/CVPRW.2009.5206552

}

TY - GEN

T1 - Global active contour-based image segmentation via probability alignment

AU - Myronenko, Andriy

AU - Song, Xubo

PY - 2009

Y1 - 2009

N2 - Active contours is a popular technique for image segmentation. However, active contour tend to converge to the closest local minimum of its energy function and often requires a close boundary initialization. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the active contour as a mean curve of the probability density function p(x). It moves to minimize the Kullback-Leibler (KL) divergence between p(x) and the probability density function derived from the image. KL divergence forces p(x) to .cover all image areas. and the uncovered areas are heavily penalized, which allows the active contour to go over the edges. Also we use deterministic annealing on the width of p(x) to implement a coarse-to-fine search strategy. In the limit, when the width of p(x) goes to zero, the KL divergence function converges to the conventional external energy term (which can be seen a special case) of active contours. Our method produces robust segmentation results from arbitrary initialization positions.

AB - Active contours is a popular technique for image segmentation. However, active contour tend to converge to the closest local minimum of its energy function and often requires a close boundary initialization. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the active contour as a mean curve of the probability density function p(x). It moves to minimize the Kullback-Leibler (KL) divergence between p(x) and the probability density function derived from the image. KL divergence forces p(x) to .cover all image areas. and the uncovered areas are heavily penalized, which allows the active contour to go over the edges. Also we use deterministic annealing on the width of p(x) to implement a coarse-to-fine search strategy. In the limit, when the width of p(x) goes to zero, the KL divergence function converges to the conventional external energy term (which can be seen a special case) of active contours. Our method produces robust segmentation results from arbitrary initialization positions.

UR - http://www.scopus.com/inward/record.url?scp=70450159370&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70450159370&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2009.5206552

DO - 10.1109/CVPRW.2009.5206552

M3 - Conference contribution

AN - SCOPUS:70450159370

SN - 9781424439935

SP - 2798

EP - 2804

BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009

ER -