Bi-Polynomial Modeling of Low-Frequency Reflectances
We present a bi-polynomial reflectance model that can precisely represent the low-frequency component of reflectance. Most existing reflectance models aim at accurately representing the complete reflectance domain for photo-realistic rendering purposes. In contrast, our bi-polynomial model is develo...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2014-06, Vol.36 (6), p.1078-1091 |
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| container_end_page | 1091 |
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| container_issue | 6 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
| container_volume | 36 |
| creator | Boxin Shi Ping Tan Matsushita, Yasuyuki Ikeuchi, Katsushi |
| description | We present a bi-polynomial reflectance model that can precisely represent the low-frequency component of reflectance. Most existing reflectance models aim at accurately representing the complete reflectance domain for photo-realistic rendering purposes. In contrast, our bi-polynomial model is developed for the purpose of accurately solving inverse problems by effectively discarding the high-frequency component while retaining nonlinear variations in the low-frequency part. The bi-polynomial reflectance model is useful for estimating reflectance and shape of an object. Experimental evaluation in comparison with other parametric reflectance models demonstrates that the proposed model achieves better performance in reflectometry and photometric stereo applications. |
| doi_str_mv | 10.1109/TPAMI.2013.196 |
| format | Article |
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Most existing reflectance models aim at accurately representing the complete reflectance domain for photo-realistic rendering purposes. In contrast, our bi-polynomial model is developed for the purpose of accurately solving inverse problems by effectively discarding the high-frequency component while retaining nonlinear variations in the low-frequency part. The bi-polynomial reflectance model is useful for estimating reflectance and shape of an object. Experimental evaluation in comparison with other parametric reflectance models demonstrates that the proposed model achieves better performance in reflectometry and photometric stereo applications.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>EISSN: 2160-9292</identifier><identifier>DOI: 10.1109/TPAMI.2013.196</identifier><identifier>PMID: 26353272</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>and thresholding ; Applied sciences ; Artificial intelligence ; Brain modeling ; color ; Computational modeling ; Computer science; control theory; systems ; Exact sciences and technology ; Intelligence ; Intensity ; Inverse problems ; Lighting ; Materials ; Mathematical model ; Nonlinearity ; Pattern analysis ; Pattern recognition. Digital image processing. 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Most existing reflectance models aim at accurately representing the complete reflectance domain for photo-realistic rendering purposes. In contrast, our bi-polynomial model is developed for the purpose of accurately solving inverse problems by effectively discarding the high-frequency component while retaining nonlinear variations in the low-frequency part. The bi-polynomial reflectance model is useful for estimating reflectance and shape of an object. Experimental evaluation in comparison with other parametric reflectance models demonstrates that the proposed model achieves better performance in reflectometry and photometric stereo applications.</description><subject>and thresholding</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Brain modeling</subject><subject>color</subject><subject>Computational modeling</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Intelligence</subject><subject>Intensity</subject><subject>Inverse problems</subject><subject>Lighting</subject><subject>Materials</subject><subject>Mathematical model</subject><subject>Nonlinearity</subject><subject>Pattern analysis</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Photometry</subject><subject>Polynomials</subject><subject>Reflectance</subject><subject>Reflectivity</subject><subject>Rendering</subject><subject>Shape</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0E1LAzEQBuAgitbq1YsgBRG8bM0k2XwcVawKLRbRc8hmZ2Vlu9FNi_Tfm9qq4MVTDvPM8OYl5AjoEICai6fp5eR-yCjwIRi5RXpguMl4zs026VGQLNOa6T2yH-MrpSByynfJHpNJMMV6RFzV2TQ0yzbMatcMJqHEpm5fBqEajMNHNurwfYGtXw4esWrQz13rMR6Qnco1EQ83b588j26eru-y8cPt_fXlOPOCynmGxqhCSA9Uo8qB-1Ih0w6rAqpVFMeVL4Tm0jg0KZCrlDLI0FEPaankfXK-vvvWhRQjzu2sjh6bxrUYFtGCAsh5Lpn4n0pptDa54Ime_qGvYdG16SMWciEpZUKrpIZr5bsQY4eVfevqmeuWFqhdVW-_qrer6m2qPi2cbM4uihmWP_y76wTONsBF75qqS13W8ddpSblmeXLHa1cj4s9YSka1ovwTeJaRpg</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Boxin Shi</creator><creator>Ping Tan</creator><creator>Matsushita, Yasuyuki</creator><creator>Ikeuchi, Katsushi</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Computational geometry</topic><topic>Photometry</topic><topic>Polynomials</topic><topic>Reflectance</topic><topic>Reflectivity</topic><topic>Rendering</topic><topic>Shape</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boxin Shi</creatorcontrib><creatorcontrib>Ping Tan</creatorcontrib><creatorcontrib>Matsushita, Yasuyuki</creatorcontrib><creatorcontrib>Ikeuchi, Katsushi</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>Pascal-Francis</collection><collection>PubMed</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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Boxin Shi</au><au>Ping Tan</au><au>Matsushita, Yasuyuki</au><au>Ikeuchi, Katsushi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bi-Polynomial Modeling of Low-Frequency Reflectances</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2014-06-01</date><risdate>2014</risdate><volume>36</volume><issue>6</issue><spage>1078</spage><epage>1091</epage><pages>1078-1091</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>We present a bi-polynomial reflectance model that can precisely represent the low-frequency component of reflectance. Most existing reflectance models aim at accurately representing the complete reflectance domain for photo-realistic rendering purposes. In contrast, our bi-polynomial model is developed for the purpose of accurately solving inverse problems by effectively discarding the high-frequency component while retaining nonlinear variations in the low-frequency part. The bi-polynomial reflectance model is useful for estimating reflectance and shape of an object. Experimental evaluation in comparison with other parametric reflectance models demonstrates that the proposed model achieves better performance in reflectometry and photometric stereo applications.</abstract><cop>Los Alamitos, CA</cop><pub>IEEE</pub><pmid>26353272</pmid><doi>10.1109/TPAMI.2013.196</doi><tpages>14</tpages></addata></record> |
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| identifier | ISSN: 0162-8828 |
| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2014-06, Vol.36 (6), p.1078-1091 |
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| source | IEEE Electronic Library (IEL) |
| subjects | and thresholding Applied sciences Artificial intelligence Brain modeling color Computational modeling Computer science control theory systems Exact sciences and technology Intelligence Intensity Inverse problems Lighting Materials Mathematical model Nonlinearity Pattern analysis Pattern recognition. Digital image processing. Computational geometry Photometry Polynomials Reflectance Reflectivity Rendering Shape |
| title | Bi-Polynomial Modeling of Low-Frequency Reflectances |
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