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Robust Shape from Polarisation and Shading
In this paper, we present an approach to robust estimation of shape from single-view multi-spectral polarisation images. The developed technique tackles the problem of recovering the azimuth angle of surface normals robust to image noise and a low degree of polarisation. We note that the linear leas...
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| creator | Cong Phuoc Huynh Robles-Kelly, Antonio Hancock, Edwin |
| description | In this paper, we present an approach to robust estimation of shape from single-view multi-spectral polarisation images. The developed technique tackles the problem of recovering the azimuth angle of surface normals robust to image noise and a low degree of polarisation. We note that the linear least-squares estimation results in a considerable phase shift from the ground truth in the presence of noise and weak polarisation in multispectral and hyper spectral imaging. This paper discusses the utility of robust statistics to discount the large error attributed to outliers and noise. Combining this approach with Shape from Shading, we fully recover the surface shape. We demonstrate the effectiveness of the robust estimator compared to the linear least-squares estimator through shape recovery experiments on both synthetic and real images. |
| doi_str_mv | 10.1109/ICPR.2010.204 |
| format | conference_proceeding |
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The developed technique tackles the problem of recovering the azimuth angle of surface normals robust to image noise and a low degree of polarisation. We note that the linear least-squares estimation results in a considerable phase shift from the ground truth in the presence of noise and weak polarisation in multispectral and hyper spectral imaging. This paper discusses the utility of robust statistics to discount the large error attributed to outliers and noise. Combining this approach with Shape from Shading, we fully recover the surface shape. We demonstrate the effectiveness of the robust estimator compared to the linear least-squares estimator through shape recovery experiments on both synthetic and real images.</description><identifier>ISSN: 1051-4651</identifier><identifier>ISBN: 1424475422</identifier><identifier>ISBN: 9781424475421</identifier><identifier>EISSN: 2831-7475</identifier><identifier>EISBN: 9781424475414</identifier><identifier>EISBN: 9780769541099</identifier><identifier>EISBN: 1424475414</identifier><identifier>EISBN: 0769541097</identifier><identifier>DOI: 10.1109/ICPR.2010.204</identifier><language>eng</language><publisher>IEEE</publisher><subject>3D Shape Recovery ; Azimuth ; Equations ; Hyperspectral Imagery ; Multispectral Imagery ; Noise ; Pixel ; Polarisation ; Robust Statistics ; Robustness ; Shape ; Shape from Shading ; Shape from X ; Surface treatment</subject><ispartof>2010 20th International Conference on Pattern Recognition, 2010, p.810-813</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5596052$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5596052$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Cong Phuoc Huynh</creatorcontrib><creatorcontrib>Robles-Kelly, Antonio</creatorcontrib><creatorcontrib>Hancock, Edwin</creatorcontrib><title>Robust Shape from Polarisation and Shading</title><title>2010 20th International Conference on Pattern Recognition</title><addtitle>ICPR</addtitle><description>In this paper, we present an approach to robust estimation of shape from single-view multi-spectral polarisation images. 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We demonstrate the effectiveness of the robust estimator compared to the linear least-squares estimator through shape recovery experiments on both synthetic and real images.</description><subject>3D Shape Recovery</subject><subject>Azimuth</subject><subject>Equations</subject><subject>Hyperspectral Imagery</subject><subject>Multispectral Imagery</subject><subject>Noise</subject><subject>Pixel</subject><subject>Polarisation</subject><subject>Robust Statistics</subject><subject>Robustness</subject><subject>Shape</subject><subject>Shape from Shading</subject><subject>Shape from X</subject><subject>Surface treatment</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>1424475422</isbn><isbn>9781424475421</isbn><isbn>9781424475414</isbn><isbn>9780769541099</isbn><isbn>1424475414</isbn><isbn>0769541097</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1jEtLw0AURq8vMNYsXbnJWki9987cmcxSio9CwVJ1XSadiQ60SUnqwn9vRF19HM7hA7ginBKhu53Plqsp44iM-ghyZyvSrLUVTfoYMq4UlXbEE7j4F8ynkBEKldoInUM-DKlGNtZYEcngZtXVn8OhePnw-1g0fbcrlt3W92nwh9S1hW_Djwupfb-Es8Zvh5j_7QTeHu5fZ0_l4vlxPrtblInY6VI5RdqFKiKT84yildoIB3a2sfUGDRsONfvAKgQXa8SxVhGRHEWjGzWB69_fFGNc7_u08_3XWsQZFFbfLOxD1A</recordid><startdate>201008</startdate><enddate>201008</enddate><creator>Cong Phuoc Huynh</creator><creator>Robles-Kelly, Antonio</creator><creator>Hancock, Edwin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201008</creationdate><title>Robust Shape from Polarisation and Shading</title><author>Cong Phuoc Huynh ; Robles-Kelly, Antonio ; Hancock, Edwin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1294-393149d8e0219a205433c52d297f7bc06262db2ad23dd9eb0049d3e00191e64f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>3D Shape Recovery</topic><topic>Azimuth</topic><topic>Equations</topic><topic>Hyperspectral Imagery</topic><topic>Multispectral Imagery</topic><topic>Noise</topic><topic>Pixel</topic><topic>Polarisation</topic><topic>Robust Statistics</topic><topic>Robustness</topic><topic>Shape</topic><topic>Shape from Shading</topic><topic>Shape from X</topic><topic>Surface treatment</topic><toplevel>online_resources</toplevel><creatorcontrib>Cong Phuoc Huynh</creatorcontrib><creatorcontrib>Robles-Kelly, Antonio</creatorcontrib><creatorcontrib>Hancock, Edwin</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 Online</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>Cong Phuoc Huynh</au><au>Robles-Kelly, Antonio</au><au>Hancock, Edwin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Robust Shape from Polarisation and Shading</atitle><btitle>2010 20th International Conference on Pattern Recognition</btitle><stitle>ICPR</stitle><date>2010-08</date><risdate>2010</risdate><spage>810</spage><epage>813</epage><pages>810-813</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>1424475422</isbn><isbn>9781424475421</isbn><eisbn>9781424475414</eisbn><eisbn>9780769541099</eisbn><eisbn>1424475414</eisbn><eisbn>0769541097</eisbn><abstract>In this paper, we present an approach to robust estimation of shape from single-view multi-spectral polarisation images. The developed technique tackles the problem of recovering the azimuth angle of surface normals robust to image noise and a low degree of polarisation. We note that the linear least-squares estimation results in a considerable phase shift from the ground truth in the presence of noise and weak polarisation in multispectral and hyper spectral imaging. This paper discusses the utility of robust statistics to discount the large error attributed to outliers and noise. Combining this approach with Shape from Shading, we fully recover the surface shape. We demonstrate the effectiveness of the robust estimator compared to the linear least-squares estimator through shape recovery experiments on both synthetic and real images.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2010.204</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1051-4651 |
| ispartof | 2010 20th International Conference on Pattern Recognition, 2010, p.810-813 |
| issn | 1051-4651 2831-7475 |
| language | eng |
| recordid | cdi_ieee_primary_5596052 |
| source | IEEE Xplore All Conference Series |
| subjects | 3D Shape Recovery Azimuth Equations Hyperspectral Imagery Multispectral Imagery Noise Pixel Polarisation Robust Statistics Robustness Shape Shape from Shading Shape from X Surface treatment |
| title | Robust Shape from Polarisation and Shading |
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