Image segmentation by polygonal Markov Fields

Kluszczyński, Rafał; Lieshout, van Mnm Marie-Colette; Schreiber, Tomasz

doi:10.1007/s10463-006-0062-8

Cited by 17 publications

(26 citation statements)

References 17 publications

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“…These were introduced a decade later in a series of our joint papers with M.N.M. van Lieshout and R. Kluszczynski [256,257,377,380,424] where a polygonal field optimization approach for image segmentation was advocated. Although these methods were quite succesful in global shape recognition, the problem we faced in that work was related to the lack of local parametrization tools designed to deal with intermediate scale image characteristics-even though the applied simulated annealing algorithm would eventually converge to the target polygonal segmentation, we were looking for a more efficient explicit mechanism to drive the local search.…”

Section: Resultsmentioning

confidence: 99%

“…Next, in Sects. 15.4 and 15.5 we develop a Markovian optimization dynamics for image segmentation, under which both the polygonal configuration and the underlying local activity function are subject to optimization-whereas the polygonal configuration evolves according to a simulated annealing scheme in the spirit of [256,257], the local activity function is initially chosen to reflect the image gradient information, whereupon it undergoes adaptive updates in the spirit of the celebrated Chen algorithm, see [73] and 10.2.4.c. in [333], with the activity profile reinforced along polygonal paths contributing to the improvement of the overall segmentation quality and faded along paths which deteriorate the segmentation quality.…”

Section: Resultsmentioning

confidence: 99%

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Mathematical Methods for Signal and Image Analysis and Representation

Florack¹,

Duits²,

Jongbloed³

et al. 2011

Computational Imaging and Vision

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This comprehensive book series embraces state-of-the-art expository works and advanced research monographs on any aspect of this interdisciplinary field.Topics covered by the series fall in the following four main categories:• Imaging Systems and Image Processing • Computer Vision and Image Understanding • Visualization •Applications of Imaging TechnologiesOnly monographs or multi-authored books that have a distinct subject area, that is where each chapter has been invited in order to fulfill this purpose, will be considered for the series. Volume 41 For further volumes: www.springer.com/series/5754 Luc Florack r Remco Duits r Geurt Jongbloed r Marie-Colette van Lieshout r Laurie Davies Editors

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mathematical Methods for Signal and Image Analysis and Representation

Florack¹,

Duits²,

Jongbloed³

et al. 2011

Computational Imaging and Vision

View full text Add to dashboard Cite

show abstract

“…The idea of using polygonal Markov field models for this purpose can be traced back to Clifford and Middleton (1989); see also Clifford and Nicholls (1994). Since the Monte Carlo methods employed at that time turned out to be rather onerous, the theme was not picked up again until the mid-2000s (Paskin and Thrun, 2005) when further theoretical results (Schreiber, 2005) motivated the development of conceptually and computationally easier algorithms (Kluszczyński et al, 2005(Kluszczyński et al, , 2007Schreiber and Lieshout, 2010;Lieshout, 2012). In the meantime, Voronoi (Green, 1995;Heikkinen and Arjas, 1998;Møller and Skare, 2001) and triangulation (Nicholls, 1998) models had also been tried.…”

Section: Discussionmentioning

confidence: 99%

An introduction to planar random tessellation models

Lieshout

2012

Spatial Statistics

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a b s t r a c tThe goal of this paper is to give an overview of random tessellation models. We discuss the classic isotropic Poisson line tessellation in some detail and then move on to more complicated models, including Arak-Clifford-Surgailis polygonal Markov fields and their Gibbs field counterparts, crystal growth models such as the Poisson-Voronoi, Johnson-Mehl and Laguerre random tessellations, and the STIT nesting scheme. An extensive list of references is included as a guide to the literature.

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“…Papers in this direction include those by Clifford & Middleton (), by Clifford & Nicholls (), by Kluszczyński et al. () and by Paskin & Thrun ().…”

Section: Introductionmentioning

confidence: 99%

Discrete Multicolour Random Mosaics with an Application to Network Extraction

Lieshout

2013

Scandinavian J Statistics

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ABSTRACT. We introduce a class of random fields that can be understood as discrete versions of multicolour polygonal fields built on regular linear tessellations. We focus first on a subclass of consistent polygonal fields, for which we show Markovianity and solvability by means of a dynamic representation. This representation is used to design new sampling techniques for Gibbsian modifications of such fields, a class which covers lattice-based random fields. A flux-based modification is applied to the extraction of the field tracks network from a Synthetic Aperture Radar image of a rural area.

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Image segmentation by polygonal Markov Fields

Cited by 17 publications

References 17 publications

Mathematical Methods for Signal and Image Analysis and Representation

Mathematical Methods for Signal and Image Analysis and Representation

An introduction to planar random tessellation models

Discrete Multicolour Random Mosaics with an Application to Network Extraction

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