Statistical Shape Modelling and Markov Random Field Restoration (invited tutorial and exercise)

Klaus Baggesen Hilger

    Research output: Contribution to conferencePosterResearch

    Abstract

    This tutorial focuses on statistical shape analysis using point distribution models (PDM) which is widely used in modelling biological shape variability over a set of annotated training data. Furthermore, Active Shape Models (ASM) and Active Appearance Models (AAM) are based on PDMs and have proven themselves a generic holistic tool in various segmentation and simulation studies. Finding a basis of homologous points is a fundamental issue in PDMs which effects both alignment and decomposition of the training data, and may be aided by Markov Random Field Restoration (MRF) of the correspondence deformation field between shapes. The tutorial demonstrates both generative active shape and appearance models, and MRF restoration on 3D polygonized surfaces. ''Exercise: Spectral-Spatial classification of multivariate images'' From annotated training data this exercise applies spatial image restoration using Markov random field relaxation of a spectral classifier. Keywords: the Ising model, the Potts model, stochastic sampling, discriminant analysis, expectation maximization.
    Original languageEnglish
    Publication date2003
    Publication statusPublished - 2003
    EventCopenhagen Image and Signal Processing (CISP) Workshop -
    Duration: 1 Jan 2003 → …

    Conference

    ConferenceCopenhagen Image and Signal Processing (CISP) Workshop
    Period01/01/2003 → …

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