Fast maximum entropy thresholding based on two-dimensional histogram oblique segmentation in infrared imaging guidance

To deal with the low contrast infrared images in complex background for infrared imaging guidance, a new fast recursive method based on Kapur's maximum entropy threshold discriminant was presented, coarse-fine searching strategy with successive approximation was adopted to reduce the threshold searching area, and the best threshold was searched by pixel in the possible area. The analysis of the methods' complexity and the segmentation experiments of the real infrared images show that, Kapur's maximum entropy threshold discriminant is more suitable for low contrast infrared images' segmentation, the running time and memory cell needed by the presented memod are all less than existing fast maximum entropy threshod recursive methods based on two-dimensional histogram vertical or oblique segmentation, the running time is about 14% of the original method. In the result image, the noise is less, the boundary is more elaborate and complete, the applicability is stronger. The presented method meets the engineering practical requirement of the infrared imaging guidance system.