A fast matching algorithm with an adaptive window based on quasi-dense method
This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an ad...
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| creator | Guo-Zun Men Jia-Li Chai Jie Zhao |
| description | This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an additional level of incremental calculation is proposed to achieve further speed-up. The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images is less textured. In particular, with big images and a large disparity range our algorithm turns out to be significantly faster. |
| doi_str_mv | 10.1109/ICMLC.2009.5212272 |
| format | Conference Proceeding |
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The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an additional level of incremental calculation is proposed to achieve further speed-up. The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images is less textured. 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In particular, with big images and a large disparity range our algorithm turns out to be significantly faster.</description><subject>Adaptive Window and Quasi-Dense Matching</subject><subject>Application software</subject><subject>Computer vision</subject><subject>Confidence Coefficient</subject><subject>Cybernetics</subject><subject>Educational institutions</subject><subject>Incremental Computation</subject><subject>Machine learning</subject><subject>Machine learning algorithms</subject><subject>Normalized Cross Correlation</subject><subject>Optimization methods</subject><subject>Pixel</subject><subject>Stereo vision</subject><subject>Testing</subject><issn>2160-133X</issn><isbn>9781424437023</isbn><isbn>1424437024</isbn><isbn>1424437032</isbn><isbn>9781424437030</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UMtOwzAQNIJKtKU_ABf_QIq9tuv4WEU8KqXiAhK3ahOvW6MmKXWg4u-JRJnDjGakXY2GsVsp5lIKd78q1mUxByHc3IAEsHDBJlKD1soKBZds5mz-70FdsTHIhcikUu8jNhnucielM3DNZil9iAHagF2oMVsvecDU8wb7ehfbLcf9tjvGftfw08AcW44eD338piFofXfiFSbyvGv55xemmHlqE_GG-l3nb9go4D7R7KxT9vb48Fo8Z-XL06pYllmU1vSZHqosHIg6SB0sYl55SwEteKMpr7UkT7km0BCCQGNkDabW1lckdEXOqym7-_sbiWhzOMYGjz-b8zTqFzqjU8o</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Guo-Zun Men</creator><creator>Jia-Li Chai</creator><creator>Jie Zhao</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200907</creationdate><title>A fast matching algorithm with an adaptive window based on quasi-dense method</title><author>Guo-Zun Men ; Jia-Li Chai ; Jie Zhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-49526920cf14f7aa8bd7efa72d54e8c41ede84e242ff0a551c25c47dbe04be9d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive Window and Quasi-Dense Matching</topic><topic>Application software</topic><topic>Computer vision</topic><topic>Confidence Coefficient</topic><topic>Cybernetics</topic><topic>Educational institutions</topic><topic>Incremental Computation</topic><topic>Machine learning</topic><topic>Machine learning algorithms</topic><topic>Normalized Cross Correlation</topic><topic>Optimization methods</topic><topic>Pixel</topic><topic>Stereo vision</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo-Zun Men</creatorcontrib><creatorcontrib>Jia-Li Chai</creatorcontrib><creatorcontrib>Jie Zhao</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</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>Guo-Zun Men</au><au>Jia-Li Chai</au><au>Jie Zhao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A fast matching algorithm with an adaptive window based on quasi-dense method</atitle><btitle>2009 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2009-07</date><risdate>2009</risdate><volume>3</volume><spage>1641</spage><epage>1646</epage><pages>1641-1646</pages><issn>2160-133X</issn><isbn>9781424437023</isbn><isbn>1424437024</isbn><eisbn>1424437032</eisbn><eisbn>9781424437030</eisbn><abstract>This paper presents a fast quasi-dense matching algorithm using an adaptive window. The algorithm starts from a set of sparse seed matches, and then propagates to the neighboring pixels; finally the most points in the images are matched. During computing the normalized cross correlation (NCC), an additional level of incremental calculation is proposed to achieve further speed-up. The confidence coefficient is introduced and the search window is varied in inverse proportion to it. The algorithm has been tested with stereo images and the results demonstrate its accuracy and efficiency. The algorithm also can be applied to a wide range of image pairs including those with large disparity or without rectification even if part of the images is less textured. In particular, with big images and a large disparity range our algorithm turns out to be significantly faster.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2009.5212272</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 2160-133X |
| ispartof | 2009 International Conference on Machine Learning and Cybernetics, 2009, Vol.3, p.1641-1646 |
| issn | 2160-133X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive Window and Quasi-Dense Matching Application software Computer vision Confidence Coefficient Cybernetics Educational institutions Incremental Computation Machine learning Machine learning algorithms Normalized Cross Correlation Optimization methods Pixel Stereo vision Testing |
| title | A fast matching algorithm with an adaptive window based on quasi-dense method |
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