Parameter estimation for a deformable template model

Merrilee Hurn*, Ingelin Steinsland, Haavard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In recent years, a number of statistical models have been proposed for the purposes of high-level image analysis tasks such as object recognition. However, in general, these models remain hard to use in practice, partly as a result of their complexity, partly through lack of software. In this paper we concentrate on a particular deformable template model which has proved potentially useful for locating and labelling cells in microscope slides Rue and Hum (1999). This model requires the specification of a number of rather non-intuitive parameters which control the shape variability of the deformed templates. Our goal is to arrange the estimation of these parameters in such a way that the microscope user's expertise is exploited to provide the necessary training data graphically by identifying a number of cells displayed on a computer screen, but that no additional statistical input is required. In this paper we use maximum likelihood estimation incorporating the error structure in the generation of our training data.

Original languageEnglish (US)
Pages (from-to)337-346
Number of pages10
JournalStatistics and Computing
Volume11
Issue number4
DOIs
StatePublished - Jan 1 2001

Keywords

  • Bayesian image analysis
  • Confocal microscopy
  • Deformable templates
  • Markov chain monte carlo
  • Object recognition
  • Parameter estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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