Infrared object segmentation based on fuzzy enhancement and mean shift

Infrared images always have low SNR and contrast,and boundaries of infrared target are blurry.Therefore the segmentation of infrared target in complex environment is very difficult.A new algorithm for infrared target segmentation based on fuzzy enhancement and mean shift is proposed in this paper.Firstly,a new membership function of fuzzy is defined,the contrast between target and background in infrared image is improved efficiently by the enhancement method based on the fuzzy set theory,in which the disadvantages of traditional fuzzy enhancement methods are avoided.Then,the intersection of confidence intervals(ICI) rule is used to determine the bandwidth in mean shift,and a new adaptive bandwidth mean shift algorithm is presented to realize further smoothing and clustering of image.Finally,infrared target is segmented by the adaptive threshold.Experimental results indicate that the algorithm can segment the infrared target under complex environment correctly and efficiently,and the good details of target are reserved.