Study on automatic detection and recognition algorithms for vehicles and license plates using LS-SVM
Based on pattern recognition theory and least squares support vector machine(LS-SVM) technology, automatic detection, location, segmentation and recognition of vehicles and license plates characters are discussed. A new multi-sorts classification method-binary exponent classification is proposed. By...
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| creator | Guangying Ge Xinzong Bao Jing Ge |
| description | Based on pattern recognition theory and least squares support vector machine(LS-SVM) technology, automatic detection, location, segmentation and recognition of vehicles and license plates characters are discussed. A new multi-sorts classification method-binary exponent classification is proposed. By comparing LS-SVM with BP neural network in vehicle and license plates pattern recognition and classification. Experimental results showed that SVM improve recognition rate and can avoid the problem of the local optimal solution of BP network, and therefore has more practicability. |
| doi_str_mv | 10.1109/WCICA.2008.4593528 |
| format | Conference Proceeding |
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A new multi-sorts classification method-binary exponent classification is proposed. By comparing LS-SVM with BP neural network in vehicle and license plates pattern recognition and classification. 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A new multi-sorts classification method-binary exponent classification is proposed. By comparing LS-SVM with BP neural network in vehicle and license plates pattern recognition and classification. Experimental results showed that SVM improve recognition rate and can avoid the problem of the local optimal solution of BP network, and therefore has more practicability.</description><subject>Artificial neural networks</subject><subject>binary exponent classification</subject><subject>Character recognition</subject><subject>Least Squares Support Vector Machines (LS-SVM)</subject><subject>Nickel</subject><subject>Pattern recognition</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>Vehicles</subject><subject>Vehicles and License Plates detection and recognition</subject><isbn>1424421136</isbn><isbn>9781424421138</isbn><isbn>1424421144</isbn><isbn>9781424421145</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtLw0AUhUekoK39A7qZP5A4784sJagVKi7iY1mSmZt0JI-SmQj996a24NlczsfhwLkI3VKSUkrM_Vf2kj2kjBCdCmm4ZPoCzalgQjBKhbj8N1zN0PwYNISoFblCyxC-ySQhuTLqGrk8ju6A-w4XY-zbInqLHUSw0R9Z5_AAtq87f_JN3Q8-7tqAq37AP7DztoHwl2u8hS4A3jdFnNAYfFfjTZ7kn683aFYVTYDl-S7Qx9Pje7ZONm_P05JN4ulKxkRTWSjOpHVCOwtMEyYMMUI5zmylDCVaMi4nXJZUWMGnVaWzJQNJGdUlX6C7U68HgO1-8G0xHLbnF_Ff7kRYJw</recordid><startdate>200806</startdate><enddate>200806</enddate><creator>Guangying Ge</creator><creator>Xinzong Bao</creator><creator>Jing Ge</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200806</creationdate><title>Study on automatic detection and recognition algorithms for vehicles and license plates using LS-SVM</title><author>Guangying Ge ; Xinzong Bao ; Jing Ge</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-815a6325cd48dce2802490946d32cf691085235802bb14c43890bdcb2e51218b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>chi ; eng</language><creationdate>2008</creationdate><topic>Artificial neural networks</topic><topic>binary exponent classification</topic><topic>Character recognition</topic><topic>Least Squares Support Vector Machines (LS-SVM)</topic><topic>Nickel</topic><topic>Pattern recognition</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>Vehicles</topic><topic>Vehicles and License Plates detection and recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Guangying Ge</creatorcontrib><creatorcontrib>Xinzong Bao</creatorcontrib><creatorcontrib>Jing Ge</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>Guangying Ge</au><au>Xinzong Bao</au><au>Jing Ge</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Study on automatic detection and recognition algorithms for vehicles and license plates using LS-SVM</atitle><btitle>2008 7th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2008-06</date><risdate>2008</risdate><spage>3760</spage><epage>3765</epage><pages>3760-3765</pages><isbn>1424421136</isbn><isbn>9781424421138</isbn><eisbn>1424421144</eisbn><eisbn>9781424421145</eisbn><abstract>Based on pattern recognition theory and least squares support vector machine(LS-SVM) technology, automatic detection, location, segmentation and recognition of vehicles and license plates characters are discussed. A new multi-sorts classification method-binary exponent classification is proposed. By comparing LS-SVM with BP neural network in vehicle and license plates pattern recognition and classification. Experimental results showed that SVM improve recognition rate and can avoid the problem of the local optimal solution of BP network, and therefore has more practicability.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2008.4593528</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2008 7th World Congress on Intelligent Control and Automation, 2008, p.3760-3765 |
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| language | chi ; eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks binary exponent classification Character recognition Least Squares Support Vector Machines (LS-SVM) Nickel Pattern recognition Support vector machine classification Support vector machines Vehicles Vehicles and License Plates detection and recognition |
| title | Study on automatic detection and recognition algorithms for vehicles and license plates using LS-SVM |
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