Efficient Active Contour and K-Means Algorithms in Image Segmentation | Zendy

J.R. Rommelse | Zendy; Hai Xiang Lin | Zendy; Tony F. Chan | Zendy

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Efficient Active Contour and K-Means Algorithms in Image Segmentation

Author(s) -

J.R. Rommelse,

Hai Xiang Lin,

Tony F. Chan

Publication year - 2004

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2004/701965

Subject(s) - active contour model , computer science , artificial intelligence , asynchronous communication , image segmentation , cluster analysis , segmentation , segmentation based object categorization , image (mathematics) , k means clustering , pixel , domain (mathematical analysis) , scale space segmentation , algorithm , pattern recognition (psychology) , computer vision , mathematics , computer network , mathematical analysis

In this paper we discuss a classic clustering algorithm that can be used to segment images and a recently developed active contour image segmentation model. We propose integrating aspects of the classic algorithm to improve the active contour model. For the resulting CVK and B-means segmentation algorithms we examine methods to decrease the size of the image domain. The CVK method has been implemented to run on parallel and distributed computers. By changing the order of updating the pixels, it was possible to replace synchronous communication with asynchronous communication and subsequently the parallel efficiency is improved

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