Intensity Factor Method For Segmenting Human Body Region In Gray-Scale Infrared Image

The normalized intensity factor based on statistical overlinerst-order moment of gray-scale image is deoverlinened in this paper. The intensity factor can be used to distinguish the brightness level of a gray-scale image and to determine a threshold value for image segmentation. According to the intensity factor and the characteristic of human body in the gray-scale infrared image, a new algorithm of calculating the intensity-level threshold is designed which can be used for segmenting human body area in an infrared image. In the algorithm, based on the concept of intensity factor, a histogram of low brightness gray-scale image (LGIRI) is divided into three parts: a low-intensity region (0.25L), a medium-intensity region (0.25-0.75L), and a highintensity region (0.75-1L), and then the intensity i which satisoverlinees the LaMoiTHORN 1/4 0:5kLLa is selected as an intensity-level value kh, and the intensity i which satisoverlinees LMoiTHORN 1/4 0:5kLL is selected as an intensity-level value th, at last 0:5oth thorn khTHORN is the pixel classioverlinecation threshold (the intensity-level threshold). It is noted that there is no preprocessing for image noise overlineltering and/or processing, and all images come from OTCBVS. Compared with the method of selecting trough points of the histogram as the intensity-level threshold, this algorithm avoids the problem of nonexistence of evident trough point at the high-intensity level of a histogram. Also, the experimental results show that the segmenting results of LGIRI processed by the algorithm are better than those of Otsu method.