P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. Intl. Journal of Computer Vision, 61(1):55--79, January 2005. Home/Search Document Details and Download
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....broader range of vision problems. For a related generalization of particle filters to graphical models, see [9] Following our presentation of the NBP algorithm, we validate its performance on a small Gaussian network. We then show how NBP may be combined with parts based local appearance models [4, 6, 14, 22] to locate and reconstruct occluded facial features. 2. Undirected Graphical Models An undirected graph G is defined by a set of nodes V and a corresponding set of edges E . The neighborhood of a node s is defined as #(s) # t # E . Graphical models associate each node s with ....
....has a wide range of potential computer vision applications. Previously, NBP was used to estimate dense stereo depth maps [19] In this section, we use NBP to infer relationships between PCA coefficients in a component based model of the human face. The proposed model extends the approaches of [6, 14, 22] to estimate not only the location, but also the appearance of occluded parts. Parts based local appearance models share many features with the articulated models often used for visual tracking. However, they lack the implementational overhead associated with modern person trackers [17] for ....
P. F. Felzenszwalb and D. P. Huttenlocher. Pictorial structures for object recognition. Submitted to IJCV, 2003.
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P. Felzenszwalb and D. Huttenlocher. Pictorial structures for object recognition. Intl. Journal of Computer Vision, 61(1):55--79, January 2005.
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Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. IJCV 61 (2005) 55--79
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P. Felzenszwalb and D. Huttenlocher, "Pictorial structures for object recognition," in IJCV, 2003.
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