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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 308164, 10 pages
Research Article

Fast Threshold Selection Algorithm of Infrared Human Images Based on Two-Dimensional Fuzzy Tsallis Entropy

College of Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, China

Received 4 October 2013; Accepted 11 December 2013; Published 16 January 2014

Academic Editor: Chung-Hao Chen

Copyright © 2014 Dong-xue Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Infrared images are fuzzy and noisy by nature; thus the segmentation of human targets in infrared images is a challenging task. In this paper, a fast thresholding method of infrared human images based on two-dimensional fuzzy Tsallis entropy is introduced. First, to address the fuzziness of infrared image, the fuzzy Tsallis entropy of objects and that of background are defined, respectively, according to probability partition principle. Next, this newly defined entropy is extended to two dimensions to make good use of spatial information to deal with the noise in infrared images, and correspondingly a fast computation method of two-dimensional fuzzy Tsallis entropy is put forward to reduce its computation complexity from to . Finally, the optimal parameters of fuzzy membership function are searched by shuffled frog-leaping algorithm following maximum entropy principle, and then the best threshold of an infrared human image is computed from the optimal parameters. Compared with typical entropy-based thresholding methods by experiments, the method presented in this paper is proved to be more efficient and robust.