Infrared image segmentation method based on 2D histogram shape modification and optimal objective function  

作　　者：Songtao Liu Donghua Gao Fuliang Yin 

机构地区：[1]Faculty of Electronic Information&Electrical Engineering,Dalian University of Technology [2]Department of Information&Communication Engineering,Dalian Naval Academy

出　　处：《Journal of Systems Engineering and Electronics》2013年第3期528-536,共9页系统工程与电子技术（英文版）

基　　金：supported by the China Postdoctoral Science Foundation(20100471451);the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)

摘　　要：In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

关 键 词：infrared image segmentation 2D histogram Otsu maximum entropy maximum correlation minimum Renyi entropy. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TQ028.61[自动化与计算机技术—计算机科学与技术]