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<p>The formalism of Bayesian networks provides a very elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes, as well serving as a knowledge base. The formalism is modified to handle spatial data, and thus the application of Bayesian networks is extended to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. Perceptual organization imparts robustness, efficiency, and a qualitative and holistic nature to vision. Thus far, the approaches to the problem of perceptual organization have been purely bottom up, without much top-down knowledge-base influence, and are therefore entirely dependent on the inputs, which are obviously imperfect. The knowledge base, besides coping with such input imperfection, also makes it possible to integrate multiple organizations and form a composite organization hypothesis. The PIN imparts an active inferential and integrating nature to perceptual organization in an elegant probabilistic framework.</p>
top-down visual processes; image recognition; spatial inference; computer vision; Bayesian networks; perceptual organization; bottom-up visual processes; knowledge base; perceptual inference network; Bayes methods; computer vision; image recognition; knowledge based systems; probability; spatial reasoning

K. Boyer and S. Sarkar, "Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 256-274, 1993.
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