Markov random measure fields for image analysis
A new Bayesian formulation for the image segmentation problem is presented. It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods...
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| creator | Marroquin, J.L. Arce, E. Botello, S. |
| description | A new Bayesian formulation for the image segmentation problem is presented. It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods on synthetic images are presented, as well as realistic applications to the segmentation of magnetic resonance volumes, to motion segmentation, and to edge-preserving filtering. |
| doi_str_mv | 10.1109/ICIP.2002.1038137 |
| format | Conference Proceeding |
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It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods on synthetic images are presented, as well as realistic applications to the segmentation of magnetic resonance volumes, to motion segmentation, and to edge-preserving filtering.</description><subject>Bayesian methods</subject><subject>Computer vision</subject><subject>Image analysis</subject><subject>Image edge detection</subject><subject>Image motion analysis</subject><subject>Image segmentation</subject><subject>Magnetic field measurement</subject><subject>Magnetic resonance</subject><subject>Motion segmentation</subject><subject>Stochastic processes</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9780780376229</isbn><isbn>0780376226</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9TksKwjAUfPgBi_YA4iYXaJtfbbIuil0ILtyXQF8l2o8kKPT2ZtG1w8AMzAwMwJ7RlDGqs6qsbimnlKeMCsVEsYCIB5OoXOolxLpQNFAUR871CiKWc55IpegGYu-fNEDmMuwjyK7GvcYvcWZoxp70aPzHIWktdo0n7eiI7c0DiRlMN3nrd7BuTecxnnULh_PpXl4Si4j124W2m-r5lvif_gASGja6</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Marroquin, J.L.</creator><creator>Arce, E.</creator><creator>Botello, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2002</creationdate><title>Markov random measure fields for image analysis</title><author>Marroquin, J.L. ; Arce, E. ; Botello, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_10381373</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Bayesian methods</topic><topic>Computer vision</topic><topic>Image analysis</topic><topic>Image edge detection</topic><topic>Image motion analysis</topic><topic>Image segmentation</topic><topic>Magnetic field measurement</topic><topic>Magnetic resonance</topic><topic>Motion segmentation</topic><topic>Stochastic processes</topic><toplevel>online_resources</toplevel><creatorcontrib>Marroquin, J.L.</creatorcontrib><creatorcontrib>Arce, E.</creatorcontrib><creatorcontrib>Botello, S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Marroquin, J.L.</au><au>Arce, E.</au><au>Botello, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Markov random measure fields for image analysis</atitle><btitle>Proceedings. International Conference on Image Processing</btitle><stitle>ICIP</stitle><date>2002</date><risdate>2002</risdate><volume>1</volume><spage>I</spage><epage>I</epage><pages>I-I</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9780780376229</isbn><isbn>0780376226</isbn><abstract>A new Bayesian formulation for the image segmentation problem is presented. It is based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators by the minimization of a differentiable function. Comparisons with existing methods on synthetic images are presented, as well as realistic applications to the segmentation of magnetic resonance volumes, to motion segmentation, and to edge-preserving filtering.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2002.1038137</doi></addata></record> |
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| ispartof | Proceedings. International Conference on Image Processing, 2002, Vol.1, p.I-I |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_1038137 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Computer vision Image analysis Image edge detection Image motion analysis Image segmentation Magnetic field measurement Magnetic resonance Motion segmentation Stochastic processes |
| title | Markov random measure fields for image analysis |
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