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
References
[1]
P.-Y. Yin, “Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization,” Signal Processing, vol. 82, no. 7, pp. 993–1006, 2002.
[2]
Z. Li, C. Liu, G. Liu, X. Yang, and Y. Cheng, “Statistical thresholding method for infrared images,” Pattern Analysis and Applications, vol. 14, no. 2, pp. 109–126, 2011.
[3]
F. Nie, C. Gao, Y. Guo, and M. Gan, “Two-dimensional minimum local cross-entropy thresholding based on co-occurrence matrix,” Computers and Electrical Engineering, vol. 37, no. 5, pp. 757–767, 2011.
[4]
J. F. Li, W. G. Gong, W. H. Li, and X. Liu, “Robust pedestrian detection in thermal infrared imagery using the wavelet transform,” Infrared Physics and Technology, vol. 53, no. 4, pp. 267–273, 2010.
[5]
F. Nie, C. Gao, and Y. Guo, “Infrared human image segmentation using fuzzy Havrda-Charv't entropy and chaos PSO algorithm,” Journal of Computer-Aided Design and Computer Graphics, vol. 22, no. 1, pp. 129–135, 2010.
[6]
M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, 2004.
[7]
M. Portes de Albuquerque, I. A. Esquef, A. R. Gesualdi Mello, and M. Portes de Albuquerque, “Image thresholding using Tsallis entropy,” Pattern Recognition Letters, vol. 25, no. 9, pp. 1059–1065, 2004.
[8]
P. K. Sahoo and G. Arora, “Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy,” Pattern Recognition Letters, vol. 27, no. 6, pp. 520–528, 2006.
[9]
A. De Luca and S. Termini, “A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory,” Information and Control, vol. 20, no. 4, pp. 301–312, 1972.
[10]
H. D. Cheng, C. H. Chen, H. H. Chiu, and H. Xu, “Fuzzy homogeneity approach to multilevel thresholding,” IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1084–1088, 1998.
[11]
W. Tao, H. Jin, and L. Liu, “Object segmentation using ant colony optimization algorithm and fuzzy entropy,” Pattern Recognition Letters, vol. 28, no. 7, pp. 788–796, 2007.
[12]
H. D. Cheng, Y. H. Chen, and X. H. Jiang, “Thresholding using two-dimensional histogram and fuzzy entropy principle,” IEEE Transactions on Image Processing, vol. 9, no. 4, pp. 732–735, 2000.
[13]
M. Zhao, A. M. N. Fu, and H. Yan, “A technique of three-level thresholding based on probability partition and fuzzy 3-partition,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 3, pp. 469–479, 2001.
[14]
M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, vol. 38, no. 2, pp. 129–154, 2006.
[15]
E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Advanced Engineering Informatics, vol. 19, no. 1, pp. 43–53, 2005.
[16]
J. W. Davis and M. A. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of the 7th IEEE Workshop on Applications of Computer Vision (WACV '05), pp. 364–369, January 2005.
[17]
V. Sharma and J. W. Davis, “Simultaneous detection and segmentation of pedestrians using top-down and bottom-up processing,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 3666–3673, June 2007.
