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Title: Unsupervised image segmentation using Markov Random Field models
Author: Barker, S. A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 1999
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The development of a fully unsupervised algorithm to achieve image segmentation is the central theme of this dissertation. Existing literature falls short of such a goal providing many algorithms capable of solving a subset of this highly challenging problem. Unsupervised segmentation is the process of identifying and locating the constituent regions of an observed image, while having no prior knowledge of the number of regions. The problem can be formulated in a Bayesian framework and through the use of an assumed model unsupervised segmentation can be posed as a problem of optimisation. This is the approach pursued throughout this dissertation. Throughout the literature, the commonly adopted model is an hierarchical image model whose underlying components are various forms of Markov Random Fields. Gaussian Markov Random Field models are used to model the textural content of the observed image's regions, while a Potts model provides a regularisation function for the segmentation. The optimisation of such highly complicated models is a topic that has challenged researchers for several decades. The contribution of this thesis is the introduction of new techniques allowing unsupervised segmentation to be carried using a single optimisation process. It is hoped that these algorithms will facilitate the future study of hierarchical image models and in particular the discovery of further models capable of more closely fitting real world data. The extensive literature surrounding Markov Random Field models and their optimisation is reviewed early in this dissertation, as is the literature concerning the selection of features to identify the textural content of an observed image. In the light of these reviews new algorithms are proposed that achieve a fusion between concepts originating in both these areas. Algorithms previously applied in statistical mechanics form an important part of this work. The use of various Markov Chain Monte Carlo algorithms is prevalent and in particular, the reversible jump sampling algorithm is of great significance. It is the combination of several of these algorithms to form a single optimisation framework that lies at the heart of the most successful algorithms presented here.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available