Obstacles Detection on a Road by Dense Stereovision with 1D Correlation Windows and Fuzzy Filtering
In this paper, we propose an original approach to obstacles detection based on stereovision with mono-dimensional correlation windows. The result of the algorithm is a dense disparity map associated with a confidence map. For each pixel, correlation indices are computed for several widths of windows...
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creator | Lefebvre, S. Ambellouis, S. Cabestaing, F. |
description | In this paper, we propose an original approach to obstacles detection based on stereovision with mono-dimensional correlation windows. The result of the algorithm is a dense disparity map associated with a confidence map. For each pixel, correlation indices are computed for several widths of windows and several positions of the window centre. Three criteria, extracted from each correlation curve, are combined by a fuzzy filter to define a confidence measure. Our 1D method is compared to a classical 2D method and shows better results in term of errors and density rate. In the context of obstacle detection, we show that a basic segmentation of our disparity map yields a better detection and marking of the obstacles. The method is validated on synthetic image sequences and our results are compared with those obtained using a classical 2D method |
doi_str_mv | 10.1109/ITSC.2006.1706830 |
format | Conference Proceeding |
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The method is validated on synthetic image sequences and our results are compared with those obtained using a classical 2D method</description><subject>Cameras</subject><subject>Computer Science</subject><subject>Engineering Sciences</subject><subject>Filtering</subject><subject>Filters</subject><subject>Image segmentation</subject><subject>Image sequences</subject><subject>Layout</subject><subject>Pixel</subject><subject>Radar detection</subject><subject>Roads</subject><subject>Signal and Image Processing</subject><subject>Stereo vision</subject><issn>2153-0009</issn><issn>2153-0017</issn><isbn>1424400937</isbn><isbn>9781424400935</isbn><isbn>1424400945</isbn><isbn>9781424400942</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkE9PwkAQxdd_iYh8AONlrx6KM53ttnskVYSEhEQwHpttu5U1tTXdCoFPbxGCySQzeb_33mEYu0MYIoJ6nC4X8dAHkEMMQUYEZ-wGhS8EgBLBOev5GJAHgOHFP6Dw8gRAXbOBc5_dBSIQhEGPZfPUtTorjeNPpjVZa-uKd6P5a61znm47uXKGL1rTmHpt3Z5vbLvi-MTjumlMqf8y77bK643jusr5-Ge32_KxLbuQrT5u2VWhS2cGx91nb-PnZTzxZvOXaTyaeSuUIXhZSqCVBKkEhn5ERumCKM0LAjSBbzKiPMw0SBAYSVUIJVIZQRRRWGikgvrs4dC70mXy3dgv3WyTWttkMpolew0g8BFRrLHz3h-81hhzMh8fS78PkWYF</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Lefebvre, S.</creator><creator>Ambellouis, S.</creator><creator>Cabestaing, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-3719-1934</orcidid><orcidid>https://orcid.org/0009-0008-5884-7307</orcidid><orcidid>https://orcid.org/0000-0002-8512-5069</orcidid></search><sort><creationdate>2006</creationdate><title>Obstacles Detection on a Road by Dense Stereovision with 1D Correlation Windows and Fuzzy Filtering</title><author>Lefebvre, S. ; Ambellouis, S. ; Cabestaing, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h1670-cb30a96069417283e9af33bdf301e52ec33d7ca06041869f494b6808837fa13f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Cameras</topic><topic>Computer Science</topic><topic>Engineering Sciences</topic><topic>Filtering</topic><topic>Filters</topic><topic>Image segmentation</topic><topic>Image sequences</topic><topic>Layout</topic><topic>Pixel</topic><topic>Radar detection</topic><topic>Roads</topic><topic>Signal and Image Processing</topic><topic>Stereo vision</topic><toplevel>online_resources</toplevel><creatorcontrib>Lefebvre, S.</creatorcontrib><creatorcontrib>Ambellouis, S.</creatorcontrib><creatorcontrib>Cabestaing, F.</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><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lefebvre, S.</au><au>Ambellouis, S.</au><au>Cabestaing, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Obstacles Detection on a Road by Dense Stereovision with 1D Correlation Windows and Fuzzy Filtering</atitle><btitle>2006 IEEE Intelligent Transportation Systems Conference</btitle><stitle>ITSC</stitle><date>2006</date><risdate>2006</risdate><spage>739</spage><epage>744</epage><pages>739-744</pages><issn>2153-0009</issn><eissn>2153-0017</eissn><isbn>1424400937</isbn><isbn>9781424400935</isbn><eisbn>1424400945</eisbn><eisbn>9781424400942</eisbn><abstract>In this paper, we propose an original approach to obstacles detection based on stereovision with mono-dimensional correlation windows. The result of the algorithm is a dense disparity map associated with a confidence map. For each pixel, correlation indices are computed for several widths of windows and several positions of the window centre. Three criteria, extracted from each correlation curve, are combined by a fuzzy filter to define a confidence measure. Our 1D method is compared to a classical 2D method and shows better results in term of errors and density rate. In the context of obstacle detection, we show that a basic segmentation of our disparity map yields a better detection and marking of the obstacles. 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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Cameras Computer Science Engineering Sciences Filtering Filters Image segmentation Image sequences Layout Pixel Radar detection Roads Signal and Image Processing Stereo vision |
title | Obstacles Detection on a Road by Dense Stereovision with 1D Correlation Windows and Fuzzy Filtering |
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