Kernel based image registration versus MLESAC: A comparative study
This paper evaluates the performance of a nonparametric robust image registration method based on the mean shift algorithm, which could successfully replace the random sampling algorithms. Therefore it realizes a comparative study between the proposed nonparametric method and other two important rob...
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| creator | Fuiorea, D. Gui, V. Pescaru, D. Toma, C. |
| description | This paper evaluates the performance of a nonparametric robust image registration method based on the mean shift algorithm, which could successfully replace the random sampling algorithms. Therefore it realizes a comparative study between the proposed nonparametric method and other two important robust random sampling methods, RANSAC and MLESAC. These techniques are analyzed and tested for performance evaluation in several image registration scenarios. |
| doi_str_mv | 10.1109/SACI.2009.5136252 |
| format | Conference Proceeding |
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Therefore it realizes a comparative study between the proposed nonparametric method and other two important robust random sampling methods, RANSAC and MLESAC. These techniques are analyzed and tested for performance evaluation in several image registration scenarios.</description><subject>Cost function</subject><subject>Image registration</subject><subject>Image sampling</subject><subject>Iterative algorithms</subject><subject>Kernel</subject><subject>Maximum likelihood estimation</subject><subject>Noise robustness</subject><subject>Parameter estimation</subject><subject>Probability density function</subject><subject>Solid modeling</subject><isbn>1424444772</isbn><isbn>9781424444779</isbn><isbn>1424444780</isbn><isbn>9781424444786</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFT8tOwzAQNEKVoKUfgLj4BxLWryTmFqIWKoI40Hvl2OsqqC_ZaaX-Pa6oxFxWMzva2SHkkUHOGOjn77pZ5BxA54qJgit-Q8ZMcplQVnD7T0o-IuOLUYMQCu7INMYfSJCKJ-mevH5g2OGGdiaio_3WrJEGXPdxCGbo9zt6whCPkX62sxT6Qmtq99uDuSxPSONwdOcHMvJmE3F6nROynM-WzXvWfr0tmrrNeg1Dpl3HrLTMeCe0VqzwwNEqU7LKagfeKeiMk4AgK6dshZ00Rak956UqHBoxIU9_Z3tEXB1C-jWcV9f-4hd6oExJ</recordid><startdate>200905</startdate><enddate>200905</enddate><creator>Fuiorea, D.</creator><creator>Gui, V.</creator><creator>Pescaru, D.</creator><creator>Toma, C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200905</creationdate><title>Kernel based image registration versus MLESAC: A comparative study</title><author>Fuiorea, D. ; Gui, V. ; Pescaru, D. ; Toma, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-9db1c4c1afd399516f02ec5a718c9d0fd50bad40e048d5c8eb4a679f22756dea3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Cost function</topic><topic>Image registration</topic><topic>Image sampling</topic><topic>Iterative algorithms</topic><topic>Kernel</topic><topic>Maximum likelihood estimation</topic><topic>Noise robustness</topic><topic>Parameter estimation</topic><topic>Probability density function</topic><topic>Solid modeling</topic><toplevel>online_resources</toplevel><creatorcontrib>Fuiorea, D.</creatorcontrib><creatorcontrib>Gui, V.</creatorcontrib><creatorcontrib>Pescaru, D.</creatorcontrib><creatorcontrib>Toma, C.</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>Fuiorea, D.</au><au>Gui, V.</au><au>Pescaru, D.</au><au>Toma, C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Kernel based image registration versus MLESAC: A comparative study</atitle><btitle>2009 5th International Symposium on Applied Computational Intelligence and Informatics</btitle><stitle>SACI</stitle><date>2009-05</date><risdate>2009</risdate><spage>255</spage><epage>260</epage><pages>255-260</pages><isbn>1424444772</isbn><isbn>9781424444779</isbn><eisbn>1424444780</eisbn><eisbn>9781424444786</eisbn><abstract>This paper evaluates the performance of a nonparametric robust image registration method based on the mean shift algorithm, which could successfully replace the random sampling algorithms. Therefore it realizes a comparative study between the proposed nonparametric method and other two important robust random sampling methods, RANSAC and MLESAC. These techniques are analyzed and tested for performance evaluation in several image registration scenarios.</abstract><pub>IEEE</pub><doi>10.1109/SACI.2009.5136252</doi><tpages>6</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Cost function Image registration Image sampling Iterative algorithms Kernel Maximum likelihood estimation Noise robustness Parameter estimation Probability density function Solid modeling |
| title | Kernel based image registration versus MLESAC: A comparative study |
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