Statistical thresholding method for infrared images
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.
infrared image standard deviation bilevel thresholding image segmentation statistical theory