Statistical thresholding method for infrared images
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Conventional statistical thresholding methods use class variance sum as criterions for threshold selection. These approaches neglect specific characteristic of practical images and fail to obtain satisfactory results when segmenting some images with similar statistical distributions in the object and background. To eliminate the limitation, a novel statistical criterion is defined by utilizing standard deviations of two thresholded classes, and the optimal threshold is determined by optimizing the criterion. The proposed method was compared with several classic thresholding counterparts on a variety of infrared images as well as general real-world ones, and the experimental results demonstrate its superiority.
KeywordsBilevel thresholding Image segmentation Standard deviation Statistical theory Infrared image
This work is supported by National Natural Science Foundation of China (Grant Nos. 60472061, 60632050, 90820004, 60875010), National 863 Project (Grant Nos. 2006AA04Z238, 2006AA01Z119), Technology Project of provincial university of Fujian Province (2008F5045, 2007F5083), Technology Startup Project of Minjiang University (YKQ07001) and Nanjing Institute of Technology Internal Fund (KXJ06037).