Texture modeling by multiple pairwise pixel interactions

A Markov random field model with a Gibbs probability distribution (GPD) is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures. The model takes into account only multiple short- and long-range pairwise interactions between the gray...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 1996-11, Vol.18 (11), p.1110-1114
1. Verfasser:	Gimel'farb, G.L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Gaussian processes Gray-scale Image generation Lattices Markov random fields Maximum likelihood estimation Parameter estimation Physics Probability distribution Stochastic processes
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container_title	IEEE transactions on pattern analysis and machine intelligence
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creator	Gimel'farb, G.L.
description	A Markov random field model with a Gibbs probability distribution (GPD) is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures. The model takes into account only multiple short- and long-range pairwise interactions between the gray levels in the pixels. An effective learning scheme is introduced to recover structure and strength of the interactions using maximal likelihood estimates of the potentials in the GPD as desired parameters. The scheme is based on an analytic initial approximation of the estimates and their subsequent refinement by a stochastic approximation. Experiments in modeling natural textures show the utility of the proposed model.
doi_str_mv	10.1109/34.544081
format	Article
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Experiments in modeling natural textures show the utility of the proposed model.</description><subject>Gaussian processes</subject><subject>Gray-scale</subject><subject>Image generation</subject><subject>Lattices</subject><subject>Markov random fields</subject><subject>Maximum likelihood estimation</subject><subject>Parameter estimation</subject><subject>Physics</subject><subject>Probability distribution</subject><subject>Stochastic processes</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNqF0LtLBDEQBvAgCp6nha3VVoLFnpPXblLK4QsObM46ZHMTiezLZBfv_ntX9rC1moH58cF8hFxTWFEK-p6LlRQCFD0hC6q5zrnk-pQsgBYsV4qpc3KR0icAFRL4gqgt7ocxYtZ0O6xD-5FVh6wZ6yH0NWa9DfE7pGkJe6yz0A4YrRtC16ZLcuZtnfDqOJfk_elxu37JN2_Pr-uHTe44qCEvkXlwVtidl5VmWqOiBQr0pdSVLG3hSgBhqQbrvHIeEKrS-p1n1fQPZXxJbufcPnZfI6bBNCE5rGvbYjcmw1QhS03l_7AQXAPnE7yboYtdShG96WNobDwYCua3RMOFmUuc7M1sAyL-uePxByC-bJ0</recordid><startdate>19961101</startdate><enddate>19961101</enddate><creator>Gimel'farb, G.L.</creator><general>IEEE</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19961101</creationdate><title>Texture modeling by multiple pairwise pixel interactions</title><author>Gimel'farb, G.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-7e2f0ca4adf5b9299e816e4ef759b57a6c7004a190acf8cf0e0b7afdf2b110123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Gaussian processes</topic><topic>Gray-scale</topic><topic>Image generation</topic><topic>Lattices</topic><topic>Markov random fields</topic><topic>Maximum likelihood estimation</topic><topic>Parameter estimation</topic><topic>Physics</topic><topic>Probability distribution</topic><topic>Stochastic processes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gimel'farb, G.L.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gimel'farb, G.L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Texture modeling by multiple pairwise pixel interactions</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><date>1996-11-01</date><risdate>1996</risdate><volume>18</volume><issue>11</issue><spage>1110</spage><epage>1114</epage><pages>1110-1114</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>A Markov random field model with a Gibbs probability distribution (GPD) is proposed for describing particular classes of grayscale images which can be called spatially uniform stochastic textures. 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subjects	Gaussian processes Gray-scale Image generation Lattices Markov random fields Maximum likelihood estimation Parameter estimation Physics Probability distribution Stochastic processes
title	Texture modeling by multiple pairwise pixel interactions
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