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Statistical thresholding method for infrared images

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Abstract

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.

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Acknowledgments

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).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China

    Zuoyong Li, Chuancai Liu, Xibei Yang & Yong Cheng

  2. Department of Computer Science, Minjiang University, 350108, Fuzhou, China

    Zuoyong Li

  3. School of Computer Science and Information Technology, Guangxi Normal University, 541004, Guilin, China

    Guanghai Liu

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  1. Zuoyong Li
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  2. Chuancai Liu
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Correspondence to Zuoyong Li.

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Li, Z., Liu, C., Liu, G. et al. Statistical thresholding method for infrared images. Pattern Anal Applic 14, 109–126 (2011). https://doi.org/10.1007/s10044-010-0184-8

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