Parameter estimation and applications of a class of Gaussian image models

This paper discusses variations of a model of images and develops algorithms for estimation of all the parameters from the raw image data. The model is suitable for some cases of (1) lossy image compression and realistic reconstruction, (2) texture synthesis and identification, (3) classification of...

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Hauptverfasser:	Dattatreya, G.R., Xiaori Fang
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Biomedical imaging Data analysis Image analysis Image coding Image reconstruction Image texture analysis Lattices Parameter estimation Pixel Random variables
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creator	Dattatreya, G.R. Xiaori Fang
description	This paper discusses variations of a model of images and develops algorithms for estimation of all the parameters from the raw image data. The model is suitable for some cases of (1) lossy image compression and realistic reconstruction, (2) texture synthesis and identification, (3) classification of remotely sensed data, and (4) analysis of medical images. Each pixel in the image is modeled as an element of a set of very few known intensity levels (henceforth called pixel-classes) plus an independent zero mean Gaussian random variable. Different statistical structures in the two dimensional lattice of pixel-classes lead to variations in the model. The image representation problem corresponds to estimation of the parameters of the discrete random field formed by the pixel classes, and the parameters of the additive Gaussian field. The authors discuss variations of the model and corresponding applications, and develop convergent estimators for all parameters.< >
doi_str_mv	10.1109/IAI.1994.336689
format	Conference Proceeding
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The model is suitable for some cases of (1) lossy image compression and realistic reconstruction, (2) texture synthesis and identification, (3) classification of remotely sensed data, and (4) analysis of medical images. Each pixel in the image is modeled as an element of a set of very few known intensity levels (henceforth called pixel-classes) plus an independent zero mean Gaussian random variable. Different statistical structures in the two dimensional lattice of pixel-classes lead to variations in the model. The image representation problem corresponds to estimation of the parameters of the discrete random field formed by the pixel classes, and the parameters of the additive Gaussian field. 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The model is suitable for some cases of (1) lossy image compression and realistic reconstruction, (2) texture synthesis and identification, (3) classification of remotely sensed data, and (4) analysis of medical images. Each pixel in the image is modeled as an element of a set of very few known intensity levels (henceforth called pixel-classes) plus an independent zero mean Gaussian random variable. Different statistical structures in the two dimensional lattice of pixel-classes lead to variations in the model. The image representation problem corresponds to estimation of the parameters of the discrete random field formed by the pixel classes, and the parameters of the additive Gaussian field. The authors discuss variations of the model and corresponding applications, and develop convergent estimators for all parameters.< ></description><subject>Biomedical imaging</subject><subject>Data analysis</subject><subject>Image analysis</subject><subject>Image coding</subject><subject>Image reconstruction</subject><subject>Image texture analysis</subject><subject>Lattices</subject><subject>Parameter estimation</subject><subject>Pixel</subject><subject>Random variables</subject><isbn>9780818662508</isbn><isbn>0818662506</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1994</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT01rwzAUM4zBRpfzYCf_gWR2nu3Yx1K2LlDoDr2XV_t5eOSLODvs3y-000USCCEx9ixFJaVwr-22raRzqgIwxro7VrjGCiutMbUW9oEVOX-LFQpU3chH1n7ijD0tNHPKS-pxSePAcQgcp6lL_uozHyNH7jvMV7nHn5wTDnzNfxHvx0BdfmL3EbtMxT9v2On97bT7KA_HfbvbHspk3VISeAwSokcyUfgQrdfkTGisulilo6ZLEABWQGyCBw9KaENWgtJ1BDKwYS-32kRE52leJ8y_59td-APRi0tD</recordid><startdate>1994</startdate><enddate>1994</enddate><creator>Dattatreya, G.R.</creator><creator>Xiaori Fang</creator><general>IEEE Comput. Soc. Press</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1994</creationdate><title>Parameter estimation and applications of a class of Gaussian image models</title><author>Dattatreya, G.R. ; Xiaori Fang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i89t-e3cad13fcae6f0cdf8c5e96d784b845f5ebd033803f7dc3c34056e813452f3e63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1994</creationdate><topic>Biomedical imaging</topic><topic>Data analysis</topic><topic>Image analysis</topic><topic>Image coding</topic><topic>Image reconstruction</topic><topic>Image texture analysis</topic><topic>Lattices</topic><topic>Parameter estimation</topic><topic>Pixel</topic><topic>Random variables</topic><toplevel>online_resources</toplevel><creatorcontrib>Dattatreya, G.R.</creatorcontrib><creatorcontrib>Xiaori Fang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dattatreya, G.R.</au><au>Xiaori Fang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Parameter estimation and applications of a class of Gaussian image models</atitle><btitle>Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation</btitle><stitle>IAI</stitle><date>1994</date><risdate>1994</risdate><spage>18</spage><epage>23</epage><pages>18-23</pages><isbn>9780818662508</isbn><isbn>0818662506</isbn><abstract>This paper discusses variations of a model of images and develops algorithms for estimation of all the parameters from the raw image data. The model is suitable for some cases of (1) lossy image compression and realistic reconstruction, (2) texture synthesis and identification, (3) classification of remotely sensed data, and (4) analysis of medical images. Each pixel in the image is modeled as an element of a set of very few known intensity levels (henceforth called pixel-classes) plus an independent zero mean Gaussian random variable. Different statistical structures in the two dimensional lattice of pixel-classes lead to variations in the model. The image representation problem corresponds to estimation of the parameters of the discrete random field formed by the pixel classes, and the parameters of the additive Gaussian field. The authors discuss variations of the model and corresponding applications, and develop convergent estimators for all parameters.< ></abstract><pub>IEEE Comput. Soc. Press</pub><doi>10.1109/IAI.1994.336689</doi><tpages>6</tpages></addata></record>
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identifier	ISBN: 9780818662508
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language	eng
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Biomedical imaging Data analysis Image analysis Image coding Image reconstruction Image texture analysis Lattices Parameter estimation Pixel Random variables
title	Parameter estimation and applications of a class of Gaussian image models
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