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Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 5th International Wo Anand Rangarajan,Baba Vemuri,Alan L. Yuille Co

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Optimierung im Hochleistungssport, even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.
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Probabilistic Subgraph Matching Based on Convex Relaxation even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.
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Object Categorization by Compositional Graphical Modelses, a global shape model of all compositions that form an object is trained. During inference, belief propagation reconciles bottom-up feature-driven categorization with top-down category models. The system achieves a competitive recognition performance on the standard CalTech database.
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