Video object articulation using depth-based content segmentation approaches

Two efficient unsupervised video object segmentation approaches are proposed and then extensively compared in terms of computational cost and quality of segmentation results. Both methods exploit depth information. In particular a depth segments map is initially estimated by analyzing a stereoscopic pair of frames and applying a segmentation algorithm. In the first, a "constrained fusion of color segments" (CFCS), video object segmentation is performed by fusion of color segments according to a depth similarity criterion. In the second approach, first, a dilated version of the boundary of each depth segment is produced and several feature points are estimated on this dilated boundary. Then, for each initial point, a normalized motion geometric space (MGS) is created which determines the only allowed path on to which the point can move. In the last step, each initial point moves on to its MGS and stops according to a weighted stop-function. Experiments on real life stereoscopic sequences are presented to exhibit the speed and accuracy of the proposed schemes.