A new snake algorithm for object segmentation in stereo images

In this paper, we propose a novel snake algorithm for object segmentation that can be overcome the limitations found in cluttered background, overlapped object placements, and the problems of initial snake points. Our newly designed algorithm is developed by using disparity information taken from several sets of stereo images. The experiment results have exhibited a better performance over the well-known snake algorithm in terms of segmentation accuracy. It is also shown that our proposed new segmentation algorithm can be extended to the tracking of object visible boundary and occlusion detection in the three dimensional disparity space.