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- W1493379661 abstract "Publisher Summary This chapter discusses the efficiency of using Bayes networks to carry out model based vision calculations. Recognition proceeds by drawing up a network model from the object's geometric and functional description that predicts the appearance of an object. Then, this network is used to find the object within a photographic image. Many existing and proposed techniques for vision recognition resemble the uncertainty calculations of a Bayes net. In contrast, they lack a derivation from first principles and tend to rely on arbitrary parameters that are expected to avoid by a network model. The connectedness of the network depends on what independence considerations can be identified in the vision problem. Greater independence leads to easier calculations, at the expense of the net's expressiveness. Once this trade-off is made and the structure of the network is determined, it should be possible to tailor a solution technique for it. The chapter discusses the use of a network with multiply connected paths, drawing on both techniques of belief networks and influence diagrams. It discusses how one formulation of a multiply connected network can be solved." @default.
- W1493379661 created "2016-06-24" @default.
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- W1493379661 date "1990-01-01" @default.
- W1493379661 modified "2023-09-23" @default.
- W1493379661 title "The structure of Bayes networks for visual recognition" @default.
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- W1493379661 doi "https://doi.org/10.1016/b978-0-444-88650-7.50035-4" @default.
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