Hidden hierarchical Markov fields for image modeling
Random heterogeneous, scale-dependent structures can be observed from many image sources, especially from remote sensing and scientific imaging. Examples include slices of porous media data showing pores of various sizes, and a remote sensing image including small and large sea-ice blocks. Meanwhile...
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ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/5772 2023-05-15T18:18:11+02:00 Hidden hierarchical Markov fields for image modeling Liu, Ying 2011-01-17 http://hdl.handle.net/10012/5772 en eng University of Waterloo http://hdl.handle.net/10012/5772 Image Modeling Markov Random Field Hidden Field Image Reconstruction Image Synthesis System Design Engineering Doctoral Thesis 2011 ftunivwaterloo 2022-06-18T22:59:02Z Random heterogeneous, scale-dependent structures can be observed from many image sources, especially from remote sensing and scientific imaging. Examples include slices of porous media data showing pores of various sizes, and a remote sensing image including small and large sea-ice blocks. Meanwhile, rather than the images of phenomena themselves, there are many image processing and analysis problems requiring to deal with \emph{discrete-state} fields according to a labeled underlying property, such as mineral porosity extracted from microscope images, or an ice type map estimated from a sea-ice image. In many cases, if discrete-state problems are associated with heterogeneous, scale-dependent spatial structures, we will have to deal with complex discrete state fields. Although scale-dependent image modeling methods are common for continuous-state problems, models for discrete-state cases have not been well studied in the literature. Therefore, a fundamental difficulty will arise which is how to represent such complex discrete-state fields. Considering the success of hidden field methods in representing heterogenous behaviours and the capability of hierarchical field methods in modeling scale-dependent spatial features, we propose a Hidden Hierarchical Markov Field (HHMF) approach, which combines the idea of hierarchical fields with hidden fields, for dealing with the discrete field modeling challenge. However, to define a general HHMF modeling structure to cover all possible situations is difficult. In this research, we use two image application problems to describe the proposed modeling methods: one for scientific image (porous media image) reconstruction and the other for remote-sensing image synthesis. For modeling discrete-state fields with a spatially separable complex behaviour, such as porous media images with nonoverlapped heterogeneous pores, we propose a Parallel HHMF model, which can decomposes a complex behaviour into a set of separated, simple behaviours over scale, and then represents each of ... Doctoral or Postdoctoral Thesis Sea ice University of Waterloo, Canada: Institutional Repository |
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University of Waterloo, Canada: Institutional Repository |
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ftunivwaterloo |
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English |
topic |
Image Modeling Markov Random Field Hidden Field Image Reconstruction Image Synthesis System Design Engineering |
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Image Modeling Markov Random Field Hidden Field Image Reconstruction Image Synthesis System Design Engineering Liu, Ying Hidden hierarchical Markov fields for image modeling |
topic_facet |
Image Modeling Markov Random Field Hidden Field Image Reconstruction Image Synthesis System Design Engineering |
description |
Random heterogeneous, scale-dependent structures can be observed from many image sources, especially from remote sensing and scientific imaging. Examples include slices of porous media data showing pores of various sizes, and a remote sensing image including small and large sea-ice blocks. Meanwhile, rather than the images of phenomena themselves, there are many image processing and analysis problems requiring to deal with \emph{discrete-state} fields according to a labeled underlying property, such as mineral porosity extracted from microscope images, or an ice type map estimated from a sea-ice image. In many cases, if discrete-state problems are associated with heterogeneous, scale-dependent spatial structures, we will have to deal with complex discrete state fields. Although scale-dependent image modeling methods are common for continuous-state problems, models for discrete-state cases have not been well studied in the literature. Therefore, a fundamental difficulty will arise which is how to represent such complex discrete-state fields. Considering the success of hidden field methods in representing heterogenous behaviours and the capability of hierarchical field methods in modeling scale-dependent spatial features, we propose a Hidden Hierarchical Markov Field (HHMF) approach, which combines the idea of hierarchical fields with hidden fields, for dealing with the discrete field modeling challenge. However, to define a general HHMF modeling structure to cover all possible situations is difficult. In this research, we use two image application problems to describe the proposed modeling methods: one for scientific image (porous media image) reconstruction and the other for remote-sensing image synthesis. For modeling discrete-state fields with a spatially separable complex behaviour, such as porous media images with nonoverlapped heterogeneous pores, we propose a Parallel HHMF model, which can decomposes a complex behaviour into a set of separated, simple behaviours over scale, and then represents each of ... |
format |
Doctoral or Postdoctoral Thesis |
author |
Liu, Ying |
author_facet |
Liu, Ying |
author_sort |
Liu, Ying |
title |
Hidden hierarchical Markov fields for image modeling |
title_short |
Hidden hierarchical Markov fields for image modeling |
title_full |
Hidden hierarchical Markov fields for image modeling |
title_fullStr |
Hidden hierarchical Markov fields for image modeling |
title_full_unstemmed |
Hidden hierarchical Markov fields for image modeling |
title_sort |
hidden hierarchical markov fields for image modeling |
publisher |
University of Waterloo |
publishDate |
2011 |
url |
http://hdl.handle.net/10012/5772 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://hdl.handle.net/10012/5772 |
_version_ |
1766194653040738304 |