Li, Chang-Tsun (1998) Unsupervised texture segmentation using multiresolution Markov random fields. PhD thesis, University of Warwick.

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Abstract

In this thesis, a multiresolution Markov Random Field (MMRF) model for
segmenting textured images without supervision is proposed. Stochastic relaxation
labelling is adopted to assign the class label with highest probability
to the block (site) being visited. Class information is propagated from low
spatial resolution to high spatial resolution, via appropriate modifications to
the interaction energies defining the field, to minimise class-position uncertainty.
The thesis contains novel ideas presented in Chapter 4 and 5, respectively.
In Chapter 4, the Multiresolution Fourier Transform (MFT) is used
to provide a set of spatially localised texture descriptors, which are based
on a two-component model of texture, in which one component is a deformation,
representing the structural or deterministic elements and the other
is a stochastic one. Experiments show that the algorithm is efficient in alleviating
class-position uncertainty via data propagation across resolutions.
However, the blocking artifacts of the segmentation results show that it is
preferable to combine both class and position information so as to achieve
smoother and more accurate boundary estimation.
In Chapter 5, based on the same MFT-MMRF framework, a boundary
process is proposed to refine the segmentation result of the region process
proposed in Chapter 4. At each resolution, all the image blocks on either
sides of the preliminary boundary detected in the region process are treated
as potential boundary-containing blocks (PBCB's). The orientation and the
centroid of the boundary-segment contained in each PBCB are calculated.
The sequence of PBCB's are then modelled as a MRF and the interaction
energy between each pair of neighbouring blocks is defined as a function of
the 'distance' D between the centroids of the two boundary segments. During
the stochastic relaxation process boundary/non-boundary labels are assigned
to the PBCB's. Once the algorithm converges, the centroids of the identified
true boundary blocks are connected to form the refined boundary which is
propagated down to the next resolution for further refinement.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Texture mapping, Image processing, Markov processes
Official Date:	1998
Dates:	Date Event 1998 Submitted
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Wilson, Roland, 1949-
Sponsors:	Government of the Republic of China on Taiwan
Extent:	vi, 159 p.
Language:	eng

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