Computer Vision-Guided Virtual Craniofacial Surgery, 2011 A Graph-Theoretic and Statistical Perspective Advances in Computer Vision and Pattern Recognition Series

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 detailed coverage of theoretical issues, the work incorporates useful algorithms and relevant concepts from both graph theory and statistics. Topics and features: presents practical solutions for virtual craniofacial reconstruction and computer-aided fracture detection; discusses issues of image registration, object reconstruction, combinatorial pattern matching, and detection of salient points and regions in an image; investigates the concepts of maximum-weight graph matching, maximum-cardinality minimum-weight matching for a bipartite graph, determination of minimum cut in a flow network, and construction of automorphs of a cycle graph; examines the techniques of Markov random fields, hierarchical Bayesian restoration, Gibbs sampling, and Bayesian inference.

The only book to treat the problem of virtual reconstructive craniofacial surgery from a combinatorial and algorithmic perspective Provides a survey of the applications of computer vision and pattern recognition to virtual surgery Contains an extensive treatment of the problems of fracture detection and virtual reconstruction Includes supplementary material: sn.pub/extras