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Proceedings Paper

Improved optimal dichotomy algorithm for image segmentation
Author(s): Chu Chen; Wei Gu; Yi Shi; WeiJiang Wang
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Paper Abstract

The performance of the classic split-and-merge segmentation algorithm is hampered by its rigid split-and-merge processes, which is insensitive to the image semantics. This paper proposes efficient algorithm and computing structure to optimize the split-and-merge processes by using the optimal dichotomy based on parallel computing. Compared to the common quadtree method, the optimal dichotomy split algorithm is shown to be more adaptive to the image semantics, which means it can avoid excessive split to some degree. We also overcome the problem that the merge iteration process requires too much by diving the image into some fixed width and height sub-images, these sub-images have one pixel wide boarder overlapped to confirm the edge information not lost. Based on the parallel computing model and platform, these sub-images’ edge can be detected within the map procedure rapidly, then we reduce the sub-images’ edges to get the whole final image segment result.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331F (29 August 2016); doi: 10.1117/12.2244944
Show Author Affiliations
Chu Chen, Beijing Institute of Technology (China)
Wei Gu, Beijing Institute of Technology (China)
Yi Shi, Beijing Institute of Technology (China)
WeiJiang Wang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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