On parameter estimation in deformable models

Rune Fisker, Jens Michael Carstensen

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    Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian formulation of deformable templates. In the supervised estimation the parameters are estimated using a likelihood and a least squares criterion given a training set. For most deformable template models the supervised estimation provides the opportunity for simulation of the prior model. The unsupervised method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented
    Original languageEnglish
    Title of host publicationProceedings of the 14th International Conference on Pattern Recognition
    Publication date1998
    ISBN (Print)0-8186-8512-3
    Publication statusPublished - 1998
    Event14th International Conference on Pattern Recognition - Brisbane, Australia
    Duration: 17 Aug 199820 Aug 1998
    Conference number: 14


    Conference14th International Conference on Pattern Recognition
    SeriesInternational Conference on Pattern Recognition

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