Improving Chamfer Template Matching Using Image Segmentation
This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic mode...
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| Veröffentlicht in: | IEEE signal processing letters 2018-11, Vol.25 (11), p.1635-1639 |
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| container_title | IEEE signal processing letters |
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| creator | Duc Thanh Nguyen Ngoc-Son Vu Thanh-Toan Do Thin Nguyen Yearwood, John |
| description | This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate. |
| doi_str_mv | 10.1109/LSP.2018.2862645 |
| format | Article |
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In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2018.2862645</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Chamfer template matching (CTM) ; Chamfering ; Computational modeling ; Detectors ; False alarms ; Image detection ; Image edge detection ; Image segmentation ; Object detection ; Object recognition ; Optimization ; Probabilistic methods ; Probabilistic models ; Template matching ; Training</subject><ispartof>IEEE signal processing letters, 2018-11, Vol.25 (11), p.1635-1639</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-1db4c2eb770aab2f275df7b3812ac8d874b86cf11fc4e9a18cbff71cb171947e3</citedby><cites>FETCH-LOGICAL-c291t-1db4c2eb770aab2f275df7b3812ac8d874b86cf11fc4e9a18cbff71cb171947e3</cites><orcidid>0000-0003-3498-3335 ; 0000-0002-6249-0848 ; 0000-0002-2285-2066</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8424505$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8424505$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Duc Thanh Nguyen</creatorcontrib><creatorcontrib>Ngoc-Son Vu</creatorcontrib><creatorcontrib>Thanh-Toan Do</creatorcontrib><creatorcontrib>Thin Nguyen</creatorcontrib><creatorcontrib>Yearwood, John</creatorcontrib><title>Improving Chamfer Template Matching Using Image Segmentation</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. In our method, object bounding boxes are iteratively adjusted to fit with the object images obtained from image segmentation in a probabilistic model. The proposed method was tested with state-of-the-art CTM-based object detectors. Experimental results have shown the proposed method improved the location accuracy of the object detectors and reduce the false alarms rate.</description><subject>Chamfer template matching (CTM)</subject><subject>Chamfering</subject><subject>Computational modeling</subject><subject>Detectors</subject><subject>False alarms</subject><subject>Image detection</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Object detection</subject><subject>Object recognition</subject><subject>Optimization</subject><subject>Probabilistic methods</subject><subject>Probabilistic models</subject><subject>Template matching</subject><subject>Training</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLAzEQgIMoWKt3wcuC562ZbLLJghcpPgoVhbbnkKSTdkt3tyar4L83S4uXmYH55sFHyC3QCQCtHuaLzwmjoCZMlazk4oyMQAiVs6KE81RTSfOqouqSXMW4o5QqUGJEHmfNIXQ_dbvJplvTeAzZEpvD3vSYvZvebYfOKg5x1pgNZgvcNNj2pq-79ppceLOPeHPKY7J6eV5O3_L5x-ts-jTPHaugz2FtuWNopaTGWOaZFGsvbaGAGafWSnKrSucBvONYGVDOei_BWZBQcYnFmNwf96ZXv74x9nrXfYc2ndQMEiRLxatE0SPlQhdjQK8PoW5M-NVA9eBIJ0d6cKRPjtLI3XGkRsR_XHHGBRXFH24bYmI</recordid><startdate>20181101</startdate><enddate>20181101</enddate><creator>Duc Thanh Nguyen</creator><creator>Ngoc-Son Vu</creator><creator>Thanh-Toan Do</creator><creator>Thin Nguyen</creator><creator>Yearwood, John</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-3498-3335</orcidid><orcidid>https://orcid.org/0000-0002-6249-0848</orcidid><orcidid>https://orcid.org/0000-0002-2285-2066</orcidid></search><sort><creationdate>20181101</creationdate><title>Improving Chamfer Template Matching Using Image Segmentation</title><author>Duc Thanh Nguyen ; Ngoc-Son Vu ; Thanh-Toan Do ; Thin Nguyen ; Yearwood, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-1db4c2eb770aab2f275df7b3812ac8d874b86cf11fc4e9a18cbff71cb171947e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Chamfer template matching (CTM)</topic><topic>Chamfering</topic><topic>Computational modeling</topic><topic>Detectors</topic><topic>False alarms</topic><topic>Image detection</topic><topic>Image edge detection</topic><topic>Image segmentation</topic><topic>Object detection</topic><topic>Object recognition</topic><topic>Optimization</topic><topic>Probabilistic methods</topic><topic>Probabilistic models</topic><topic>Template matching</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duc Thanh Nguyen</creatorcontrib><creatorcontrib>Ngoc-Son Vu</creatorcontrib><creatorcontrib>Thanh-Toan Do</creatorcontrib><creatorcontrib>Thin Nguyen</creatorcontrib><creatorcontrib>Yearwood, John</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Duc Thanh Nguyen</au><au>Ngoc-Son Vu</au><au>Thanh-Toan Do</au><au>Thin Nguyen</au><au>Yearwood, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improving Chamfer Template Matching Using Image Segmentation</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2018-11-01</date><risdate>2018</risdate><volume>25</volume><issue>11</issue><spage>1635</spage><epage>1639</epage><pages>1635-1639</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>This letter proposes an effective method to improve object location in Chamfer template matching (CTM) based object detection using image segmentation. 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| ispartof | IEEE signal processing letters, 2018-11, Vol.25 (11), p.1635-1639 |
| issn | 1070-9908 1558-2361 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Chamfer template matching (CTM) Chamfering Computational modeling Detectors False alarms Image detection Image edge detection Image segmentation Object detection Object recognition Optimization Probabilistic methods Probabilistic models Template matching Training |
| title | Improving Chamfer Template Matching Using Image Segmentation |
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