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Proceedings Paper

Image segmentation using an improved differential algorithm
Author(s): Hao Gao; Yujiao Shi; Dongmei Wu
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Paper Abstract

Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu’s method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu’s method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.

Paper Details

Date Published: 31 October 2014
PDF: 6 pages
Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731U (31 October 2014); doi: 10.1117/12.2071004
Show Author Affiliations
Hao Gao, Nanjing Univ. of Posts and Telecommunications (China)
Yujiao Shi, Nanjing Univ. of Posts and Telecommunications (China)
Dongmei Wu, Nanjing Univ. of Posts and Telecommunications (China)

Published in SPIE Proceedings Vol. 9273:
Optoelectronic Imaging and Multimedia Technology III
Qionghai Dai; Tsutomu Shimura, Editor(s)

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