Real-time Shape and Posture Estimation of Articulated Objects Based on Multiple kinds of Uncertain Feature Information Obtained from Image Sequences

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The summarized results of this research are as follows:1. We developed a video-rate hand posture measurement system based on hand region contour image, which consists of high speed PCs with large memories and are connected with each other by G-bit LAN system. The system can estimate almost 160000 possible shapes (including highly self-occluded hand shapes) in real-time using monocular camera setting.2. We developed an online method to refine the initial shape of the articulated model for posture estimation. The method efficiently describes a huge set of the possible posture parameters employing a multi-dimensional ellisoid description. The method can refine the given initial model shape after the posture estimation in each timestep from a monocular image sequences, while it is still computationally heavy process and cannot be processed in real-time under the current computing environment.3. We proposed a recognition method of Japanese Sign Language words using multiple camera images, color feature, image motion flows and observed number of the finger tips. Although each of these image features has uncertainty, we developed the system which integrates whole of the information using Hidden Markov Model learning and implements it into a gestural interface.4. We proposed a novel time-space gestural model based on Switching Linear Dynamics architecture. Based on this model, we developed an on-line method to segment a shape-changing human hand region from a complicated background and identify it from several gesture models.