Visual Deconstruction : Recognizing ArticulatedObjectsTyng -

We propose a deconstruction framework to recognize and nd articulated objects. In particular we are interested in human arm and leg articulations. The deconstruction view of recognition naturally decomposes the problem of nding an object in an image, into the one of (i) extracting key features in an image, (ii) detecting key points in the models, (iii) segmenting an image, and (iv) comparing shapes. All of these subproblems can not be resolved independently. Together, they reconstruct the object in the image. We brieey address (i) and (ii) to focus on solving together shape similarity and segmentation, combining top-down & bottom-up algorithms. We show that the visual deconstruction approach is derived as an optimization for a Bayesian-Information theory, and that the whole process is naturally generated by the guaranteed Dijkstra optimization algorithm.