Region extraction using competition of multiple active contour models

Most of conventional active contour models are not capable of extracting objects with complex image features or background, because they are deformed mainly based on the local image features along the contours. To deal with this problem, we propose a novel extracting method which reflects wide-ranging region information to region extracting process through competition of active contours. In the proposed method, firstly we set the initial curves in object and background, then divide these curves into segments as cores of initial contours. Secondly, we estimate feature distribution of inside of each contour, and determine the likelihood of control points to each contour with respect to image features. Each contour performs region competition based on the likelihood, and finally an object is extracted as a set of multiple active contours.