Clustering-based control of active contour model

Toru Abe, Yuki Matsuzawa

Research output: Contribution to journalArticlepeer-review


To extract object regions from images, the methods using region-based active contour model (ACM) have been proposed. By controlling ACM with the statistical characteristics of the image properties, these methods effect robust region extraction. However, the existing methods require redundant processing and cannot adapt to complex scene images. To overcome these problems, we propose a new method for controlling region-based ACM. In the proposed method, a definite area is set along an object boundary. This area is partitioned into several subareas, and they are iteratively deformed to make the image properties be uniform in each subarea. As a result of this clustering on the definite area, the image properties in a necessary and sufficient area can be effectively reflected on ACM control, and efficient and accurate region extraction can be achieved.

Original languageEnglish
Pages (from-to)663-667
Number of pages5
JournalProceedings - International Conference on Pattern Recognition
Issue number2
Publication statusPublished - 2002 Dec 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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