Object Detection and Segmentation using Local and Global Property

Object detection and segmentation are critical task for many computer vision applications; they provide classification of the pixels into either foreground or background. In this paper, a new algorithm of objects detection and segmentation is proposed. Traditional approaches to object detection only look at local pieces of the image, whether it will be within a sliding window or the regions around an interest point detector. However, such local pieces can be ambiguous, especially when the object of interest is small, or imaging conditions are otherwise unfavorable. This ambiguity can be reduced by using global features of the image.