Titlebook: Computer Vision-Guided Virtual Craniofacial Surgery; A Graph-Theoretic an Ananda S. Chowdhury,Suchendra M. Bhandarkar Book 2011 Springer-Ve

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The Energetics of Mangrove Forestsograph. The details of the graph-theoretic modeling of specific problems in the context of virtual craniofacial surgery, along with appropriate justifications, will be provided in later chapters. This chapter contains an introductory section, followed by three separate sections on graph matching, graph isomorphism, and network flow.

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The Energetics of Mangrove Forestsses Bayesian statistics, beginning with Bayes’ theorem and concluding with Bayesian inference. The last section serves as an introduction to random fields, image restoration within a Bayesian paradigm, and some associated techniques such as stochastic relaxation.

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2191-6586 a survey of the applications of computer vision and pattern This unique text/reference discusses in depth the two integral components of reconstructive surgery; fracture detection, and reconstruction from broken bone fragments. In addition to supporting its application-oriented viewpoint with detail

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Graph-Theoretic Foundationss constitute the theoretical underpinnings of a variety of techniques used to solve the various problems that arise in the context of virtual craniofacial surgery. The focus of this chapter is to present the necessary graph-theoretic foundations for the research presented in the remainder of the mon

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A Statistical Primerr contains four distinct sections. The first section deals with the basic concepts of probability and random variables. The second section is devoted to statistical inference which explains concepts such as the sampling distribution, point estimator, and confidence interval. The third section discus

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Virtual Single-Fracture Mandibular Reconstructionch of this discussion is applicable not only to situations that involve exclusively a single fracture, but also to multiple fracture scenarios wherein the fractures are fixated one at a time in the operating room. Thus, the problem of mandibular reconstruction in the case of a single fracture assume

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Virtual Multiple-Fracture Mandibular Reconstructionating surgeon has to identify the opposable fracture surfaces before physically registering them. Very often, the cost of surgery becomes prohibitive with the increased operative time necessary to complete the entire reconstruction process. Moreover, the increased operative time also poses increased

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Fracture Detection Using Bayesian Inferenceatic detection of fracture surfaces, which subsequently serve as input data to the surface matching and virtual reconstruction algorithms, is a critical and integral aspect of this endeavor. The generation of fracture surface data thus far has entailed significant user involvement. The surgeon has h

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