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Statistical Models of Shape [electronic resource] :Optimisation and Evaluation / by Chris Taylor, Carole Twining, Rhodri Davies.

by Taylor, Chris [author.]; Twining, Carole [author.]; Davies, Rhodri [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: London : Springer London, 2008.Description: online resource.ISBN: 9781848001381.Subject(s): Computer science | Computer vision | Optical pattern recognition | Computer Science | Pattern Recognition | Computer Imaging, Vision, Pattern Recognition and Graphics | Image Processing and Computer VisionOnline resources: Click here to access online
Contents:
Statistical Models of Shape and Appearance -- Establishing Correspondence -- Objective Functions -- Re-parameterisation of Open and Closed Curves -- Parameterisation and Re-parameterisation of Surfaces -- Optimisation -- Non-parametric Regularization -- Evaluation of Statistical Models.
In: Springer eBooksSummary: 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.
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Statistical Models of Shape and Appearance -- Establishing Correspondence -- Objective Functions -- Re-parameterisation of Open and Closed Curves -- Parameterisation and Re-parameterisation of Surfaces -- Optimisation -- Non-parametric Regularization -- Evaluation of Statistical Models.

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.

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