3D model-based segmentation of videoconference image sequences

This paper describes a 30 model-based unsupervised procedure for the segmentation of multiview image sequences using multiple sources of information. The articulation procedure is based on the homogeneity of parameters, such as rigid 30 motion, color and depth, estimated for each sub-object, which consists of a number of interconnected triangles of the 30 model. The rigid 30 motion of each sub-object for subsequent frames is estimated using a Kalman filtering algorithm taking into account the temporal correlation between consecutive frames. Information from all cameras is combined during the formation of the equations for the rigid 30 motion parameters. The parameter estimation for each sub-object and the 30 model segmentation procedures are interleaved and repeated iteratively until a satisfactory object segmentation emerges. The performance of the resulting segmentation method is evaluated experimentally.