We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts structure to different object classes as long as they have similar shape. The spatial layout between parts is described by a non-parametric density, which is more flexible and easier to learn than commonly used Gaussian or other parametric distributions. We express object detection as state estimation inference executed using a novel Particle Filters (PF) framework with static observations, which is quite different from previous PF methods. Although the underlying graph structure of our model is given by a fully connected graph, the proposed PF algorithm efficiently linearizes it by exploring the conditional dependencies of the nodes representing contour parts. Experimental results demonstrate that the proposed approach can not only yield very good detection results but also accurately locates contours of target objects in cluttered images.
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