Cloud Model-Based Method for Infrared Image Thresholding

Traditional statistical thresholding methods, directly constructing the optimal threshold criterion using the class variance, have certain versatility but lack the specificity of practical application in some cases. To select the optimal threshold for infrared image thresholding, a simple and efficient method based on cloud model is proposed. The method firstly generates the cloud models corresponding to image background and object, respectively, and defines a novel threshold dependence criterion related with the hyper-entropy of these cloud models and then determines the optimal grayscale threshold by the minimization of this criterion. It is indicated by the experiments that, compared with selected methods, using both image thresholding and target detection, the proposed method is suitable for infrared image thresholding since it performs good results and is reasonable and effective.

[1]  Qin Zhang,et al.  Image bilevel thresholding based on stable transition region set , 2013, Digit. Signal Process..

[2]  Patrick Siarry,et al.  Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction , 2013, Digit. Signal Process..

[3]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[4]  Guifang Guo,et al.  Cloud model-based energy management strategy for parallel hybrid vehicles , 2015 .

[5]  Guoyin Wang,et al.  p-order Normal Cloud Model Recursive Definition and Analysis of Bidirectional Cognitive Computing: p-order Normal Cloud Model Recursive Definition and Analysis of Bidirectional Cognitive Computing , 2014 .

[6]  Zhao Cai-rong.  Statistical Thresholding Method for Infrared Images , 2010 .

[7]  Yantao Wei,et al.  Small target detection based on weighted self-information map , 2013 .

[8]  Erik Cuevas,et al.  Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms , 2015 .

[9]  Zijun Feng,et al.  Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images , 2013 .

[10]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[11]  Yixiang Chen,et al.  Cloud model-based method for range-constrained thresholding , 2015, Comput. Electr. Eng..

[12]  Qingmao Hu,et al.  On minimum variance thresholding , 2006, Pattern Recognit. Lett..

[13]  Jerry M. Mendel,et al.  Type-2 fuzzy sets and systems: an overview , 2007, IEEE Computational Intelligence Magazine.

[14]  Pin Lv,et al.  Cloud theory-based simulated annealing algorithm and application , 2009, Eng. Appl. Artif. Intell..

[15]  Leo Grady,et al.  Isoperimetric graph partitioning for image segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Miin-Shen Yang,et al.  Similarity Measures Between Type-2 Fuzzy Sets , 2004, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[17]  Guoyin Wang,et al.  Generic normal cloud model , 2014, Inf. Sci..

[18]  Tao Wu,et al.  Comparative Study of Type-2 Fuzzy Sets and Cloud Model , 2010, RSKT.

[19]  Feng-Yan Yi,et al.  Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems , 2015, Comput. Intell. Neurosci..

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  Bang Jun Lei,et al.  Maximum similarity thresholding , 2014, Digit. Signal Process..

[22]  William W. Guo,et al.  Incorporating utility and cloud theories for owner evaluation in tendering , 2012, Expert Syst. Appl..

[23]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[24]  Baohua Mao,et al.  Research on CFCM: Car Following Model Using Cloud Model Theory , 2007 .

[25]  I. Sethi,et al.  Thresholding based on histogram approximation , 1995 .

[26]  Kun Qin,et al.  Comparative Study of Image Thresholding Using Type-2 Fuzzy Sets and Cloud Model , 2010 .

[27]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[28]  D. M. Titterington,et al.  Median-based image thresholding , 2011, Image Vis. Comput..

[29]  Mostafa Zandieh,et al.  Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flowshop , 2010, Adv. Eng. Softw..

[30]  Kai Xu,et al.  Image segmentation based on histogram analysis utilizing the cloud model , 2011, Comput. Math. Appl..

[31]  Sirapat Chiewchanwattana,et al.  A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding , 2013 .

[32]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..