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By Gilles Deleuze

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By Gilles Deleuze

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Therefore, we assume that D is always formed by basic components. Theorem 1. ,. n such thatr{ri{e{Bi)))=rj. The fact that we look only for rigid transformations of the complex shape D in a given image I reduces the number of possible matches between the regions of D and those of X, as stated in the following theorem. Theorem 2. Given a complex shape with k basic components and an image segmented in n regions, there are at most n2 possible matches between the k basic shapes and the n regions. The above theorem leads to the algorithm Recognize, in which we exploit the fact that each region has a centroid, and to match regions, their centroids should be matched first.

H. Coupé MC B CT ISC C SH Fig. 1. The digraph of an example belief network; it expresses information concerning the presence of a brain tumour and its associated problems in an arbitrary patient. 60 The probabilities specified for the variable ISC , for example, express that knowing whether or not metastatic cancer is present has a considerable influence on the probability of an increased level of serum calcium in an arbitrary patient. The relationship between metastatic cancer and increased total serum calcium therefore is a strong dependence.

The increasing number of knowledge-based systems that build upon the framework of Bayesian belief networks for knowledge representation and inference, clearly demonstrate its usefulness for addressing complex real-life problem domains in which uncertainty is predominant. Most notably, applications are being realised in the medical domain, for diagnosis, prognostic assessment, and treatment planning. Bayesian belief networks are generally constructed with the help of experts from the domain of application.

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