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Statistical models of shape, learnt from a set of examples, are a widely-used tool in image interpretation and shape analysis. Integral to this learning process is the establishment of a dense groupwise correspondence across the set of training examples. This book gives a comprehensive and up-to-date account of the optimisation approach to shape correspondence, and the question of evaluating the quality of the resulting model in the absence of ground-truth data. It begins with a complete account of the basics of statistical shape models, for both finite and infinite-dimensional representations of shape, and includes linear, non-linear, and kernel-based approaches to modelling distributions of shapes. The optimisation approach is then developed, with a detailed discussion of the various objective functions available for establishing correspondence, and a particular focus on the Minimum Description Length approach. Various methods for the manipulation of correspondence for shape curves and surfaces are dealt with in detail, including recent advances such as the application of fluid-based methods. This complete and self-contained account of the subject area brings together results from a fifteen-year program of research and development. It includes proofs of many of the basic results, as well as mathematical appendices covering areas which may not be totally familiar to some readers. Comprehensive implementation details are also included, along with extensive pseudo-code for the main algorithms. Graduate students, researchers, teachers, and professionals involved in either the development or the usage of statistical shape models will find this an essential resource.

Computer Science. --- Pattern Recognition. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Image Processing and Computer Vision. --- Computer science. --- Computer vision. --- Optical pattern recognition. --- Informatique --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- Mathematical optimization. --- Shape theory (Topology) --Statistical methods. --- Shape theory (Topology) --- Mathematical optimization --- Geometry --- Mathematics --- Physical Sciences & Mathematics --- Statistical methods --- Statistical methods. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Computer graphics. --- Image processing. --- Pattern recognition. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Homotopy theory --- Mappings (Mathematics) --- Topological manifolds --- Topological spaces --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Optical data processing. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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