TY - BOOK ID - 4804396 TI - Statistical models of shape : optimisation and evaluation AU - Davies, Rhodri. AU - Twining, Carole. AU - Taylor, Chris PY - 2008 SN - 9781848001381 1848001371 9781848001374 1447160428 9786611954857 1281954853 184800138X PB - London : Springer, DB - UniCat KW - Computer Science. KW - Pattern Recognition. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Image Processing and Computer Vision. KW - Computer science. KW - Computer vision. KW - Optical pattern recognition. KW - Informatique KW - Vision par ordinateur KW - Reconnaissance optique des formes (Informatique) KW - Mathematical optimization. KW - Shape theory (Topology) --Statistical methods. KW - Shape theory (Topology) KW - Mathematical optimization KW - Geometry KW - Mathematics KW - Physical Sciences & Mathematics KW - Statistical methods KW - Statistical methods. KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Computer graphics. KW - Image processing. KW - Pattern recognition. KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Homotopy theory KW - Mappings (Mathematics) KW - Topological manifolds KW - Topological spaces KW - Optical data processing KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Optical data processing. KW - Design perception KW - Pattern recognition KW - Form perception KW - Perception KW - Figure-ground perception KW - Optical computing KW - Visual data processing KW - Bionics KW - Electronic data processing KW - Integrated optics KW - Photonics KW - Computers KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:4804396 AB - 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. ER -