Bayesian and Non-Bayesian Probabilistic Models for Image Analysis

P Bromiley, NA Thacker, Marietta Scott, Maja Pokric, AJ Lacey, TF. Cootes

Research output: Contribution to journalArticlepeer-review


Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by these examples are used to illustrate difficulties in Bayesian methods and to motivate an approach based on frequentist methods. We believe this approach to be more suited to quantitative data analysis, and provide a general theory for the use of these methods in learning (Bayes risk) systems and for data fusion. Proofs are given for the more novel aspects of the theory. We conclude with a discussion of the strengths and weaknesses, and the fundamental suitability, of Bayesian and non-Bayesian approaches for MRI analysis in particular, and for machine vision systems in general.
Original languageEnglish
JournalIVC Special Edition: The use of Probabilistic Models in Computer Vision
Publication statusPublished - 2003


  • Bayesian and non-Bayesian probabilistic models
  • Magnetic resonance image analysis
  • Expectation maximisation algorithm


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