Spatiotemporal segmentation and tracking of objects for visualization of videoconference image sequences

A procedure is described for the segmentation, content-based coding and visualization of videoconference image sequences. First, image sequence analysis is used to estimate the shape and motion parameters of the person facing the camera. The foreground object is segmented in a number of subobjects, in order to identify the facial region. For this purpose, we propose the novel procedure of K-Means with connectivity constraint algorithm as a general segmentation algorithm combining several types of information including intensity, motion and compactness. In this algorithm, the use of spatiotemporal regions is introduced since a number of frames is analyzed simultaneously and as a result the same region is present in consequent frames. Based on this information, a 3D ellipsoid is adapted to the person´s face using an efficient and robust algorithm. The rigid 3D motion is estimated next using a least median of squares approach. Finally a VRML file is created containing all the above estimated information; this file may be viewed by using any VRML 2.0 compliant browser