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- W2116735535 abstract "We present an image-based approach to infer 3D structureparameters using a probabilistic shape+structure model.The 3D shape of an object class is represented by setsof contours from silhouette views simultaneously observedfrom multiple calibrated cameras, while structural featuresof interest on the object are denoted by a number of 3D locations.A prior density over the multi-view shape and correspondingstructure is constructed with a mixture of probabilisticprincipal components analyzers. Given a novelset of contours, we infer the unknown structure parametersfrom the new shape's Bayesian reconstruction. Modelmatching and parameter inference are done entirely in theimage domain and require no explicit 3D construction. Ourshape model enables accurate estimation of structure despitesegmentation errors or missing views in the input silhouettes,and it works even with only a single input view.Using a training set of thousands of pedestrian images generatedfrom a synthetic model, we can accurately infer the3D locations of 19 joints on the body based on observedsilhouette contours from real images." @default.
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- W2116735535 date "2003-10-13" @default.
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- W2116735535 title "Inferring 3D Structure with a Statistical Image-Based Shape Model" @default.
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