Segmentation of human from photo images based on mctd and color or contrast modifications

Color distribution of digital images and segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval. Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes. In this paper, we propose a straightforward framework to automatically recover human bodies from color photos and contrast equalization, midway histogram, color enhancement, and color transfer. Employing a coarse-to-fine strategy, we first detect a coarse torso (CT) using the multi cue CT detection algorithm and then extract the accurate region of the upper body. Then, an iterative multiple oblique histogram algorithm is presented to accurately recover the lower body based on human kinematics. The approach relies on the key observation that artefacts correspond to spatial irregularity of the so-called transportation map, defined as the difference between the original and the corrected image.