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Probabilistic Methods for Discrete Labeling Problems in Digital Image Processing and Analysis Open Access


Other title
Random fields
Labeling problems
Image fusion
Stereo correspondence
Random walks
Type of item
Degree grantor
University of Alberta
Author or creator
Shen, Rui
Supervisor and department
Basu, Anup (Computing Science)
Cheng, Irene (Computing Science)
Examining committee member and department
Pedrycz, Witold (Electrical and Computer Engineering)
Ray, Nilanjan (Computing Science)
Boulanger, Pierre (Computing Science)
Zhang, Cha (Microsoft Research)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Many problems in digital image processing and analysis can be interpreted as labeling problems, which aim to find the optimal mapping from a set of sites to a set of labels. A site represents a certain primitive, such as a pixel, while a label represents a certain quantity, such as disparity in stereo correspondence. Considering this labeling interpretation, instead of solving different problems individually, we propose a series of unified frameworks in the random walks (RW) context for labeling problems with regular sites and discrete labels. The first framework, which we term as the generalized random walks (GRW) framework, converts a discrete labeling problem into the calculation of steady-state probabilities of random walkers transiting on a graph. The performance of GRW is validated through experiments on stereo correspondence and multi-exposure fusion, which we formulate as labeling problems in the RW context. By incorporating hierarchical and multiscale schemes, the performance of GRW is further improved. This leads to two enhanced frameworks: the hierarchical random walks (HRW) framework, which reduces the computational cost of GRW but produces good approximations; and the multiscale random walks (MRW) framework, which produces robust solutions utilizing both inter- and intra-scale information. The performance of HRW is validated through perception-guided multi-exposure fusion, where we also introduce advanced perceptual metrics into multi-exposure fusion; the performance of MRW is verified through volumetric medical image fusion, where we also introduce a cross-scale fusion rule based on MRW. Furthermore, a multivariate Gaussian conditional random field model and its hierarchical version are proposed for more general multi-label problems, and their relationships with GRW and HRW are analyzed.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Rui Shen, Irene Cheng, Jianbo Shi, and Anup Basu. Generalized random walks for fusion of multi-exposure images. IEEE Transactions on Image Processing, vol. 20, no. 12, pages 3634-3646, 2011.

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