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We propose a novel snake algorithm for object segmentation that can overcome the limitations found in cluttered backgrounds, 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 an object visible boundary and occlusion detection in the 3D disparity space.