Abstract: In this paper, statistical shape prior and smoothness of contours are simultaneously incorporated into probabilistic contour estimation. By means of principal component analysis, shape samples are generated from sampling a density in a lower dimensional space. Based on the state samples derived from the shape samples and on the prior density encoding smoothness, the contour with a specific shape is estimated by a sequential Monte-Carlo method. Extensive experiments on the real-world hand images show that the proposed approach significantly improves the robustness to cluttered background, occlusion and noise with respect to the objects with specific shapes.