%0 Journal Article
%T Fast Threshold Selection Algorithm of Infrared Human Images Based on Two-Dimensional Fuzzy Tsallis Entropy
%A Dong-xue Xia
%A Chun-gui Li
%A Shu-hong Yang
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/308164
%X 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. 1. Introduction Image segmentation is an important topic in the field of digital image process. It intends to extract objects from background based on some pertinent characteristics in an image such as gray level, color, texture, and location [1]. Thresholding is one of the most popular segmentation approaches because of its simplicity [2, 3]. It serves a variety of applications such as biomedical image analysis, character identification, and change detection [3]. Compared with visible images, the intensity of human targets in infrared images is obviously different from that of background. Therefore, segmenting infrared human images by threshold selection is feasible [4]. Moreover, the intensity of human targets in infrared image is mainly determined by its temperature and radiated heat and is independent of the current light conditions, so the detection system can be applied indiscriminately in both day and night [4]. However, infrared images are not perfect either. Due to the limitations in camera technology, most infrared images have lower spatial resolution and less sensitivity than visible images, which often leads to poor image quality, such as blurring, low target-to-background contrast, and great noise. Therefore, it is a complex challenge to make precise segmentation of human targets in infrared images by threshold selection [5]. 2. Related Work Excellent reviews on early thresholding methods can be found in [6]. Among all the
%U http://www.hindawi.com/journals/mpe/2014/308164/