On parameter estimation in deformable models

Rune Fisker, Jens Michael Carstensen

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    Abstract

    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
    Volume1-2
    PublisherIEEE
    Publication date1998
    Pages762-766
    ISBN (Print)0-8186-8512-3
    DOIs
    Publication statusPublished - 1998
    Event14th International Conference on Pattern Recognition - Brisbane, Australia
    Duration: 1 Jan 1998 → …
    Conference number: 14

    Conference

    Conference14th International Conference on Pattern Recognition
    Number14
    CityBrisbane, Australia
    Period01/01/1998 → …
    SeriesInternational Conference on Pattern Recognition
    ISSN1051-4651

    Bibliographical note

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    Cite this

    Fisker, R., & Carstensen, J. M. (1998). On parameter estimation in deformable models. In Proceedings of the 14th International Conference on Pattern Recognition (Vol. 1-2, pp. 762-766). IEEE. International Conference on Pattern Recognition https://doi.org/10.1109/ICPR.1998.711258