rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Models of Shape
    sub title: Optimisation and Evaluation
  name:
    name part: Taylor, Chris.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Twining, Carole.
    role:
      role term: author.
  name:
    name part: Davies, Rhodri.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 302 p. online resource.
  abstract: 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.
  table of 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.
  note: by Chris Taylor, Carole Twining, Rhodri Davies.
  subject:
    topic: Computer science
  subject:
    topic: Computer graphics
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  subject:
    topic: Pattern Recognition
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781848001381
  identifier: http://dx.doi.org/10.1007/978-1-84800-138-1
  record info:
    record creation date: 100301
    record change date: 20151204150446.0
    record identifier: 978-1-84800-138-1