Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPRW.2009.5206731
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dc.titleA Projective framework for radiometric image analysis
dc.contributor.authorTan, P.
dc.contributor.authorZickler, T.
dc.date.accessioned2014-06-19T02:55:59Z
dc.date.available2014-06-19T02:55:59Z
dc.date.issued2009
dc.identifier.citationTan, P.,Zickler, T. (2009). A Projective framework for radiometric image analysis. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 : 2977-2984. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CVPRW.2009.5206731" target="_blank">https://doi.org/10.1109/CVPRW.2009.5206731</a>
dc.identifier.isbn9781424439935
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69024
dc.description.abstractDifferent materials reflect light in different ways, and reflectance interacts with shape, lighting, and viewpoint to determine an object's image. Common materials exhibit diverse reflectance effects, and this is a significant source of difficulty for radiometric image analysis. One strategy for dealing with this diversity is to build computational tools that exploit reflectance symmetries, such as reciprocity and isotropy, that are exhibited by broad classes of materials. In this paper, we advocate the real projective plane as a tool for representing and exploiting these symmetries. In this approach, each point in the plane represents a surface normal that is visible from a fixed viewpoint, and reflectance symmetries are analyzed in terms of the geometric structures that they induce. We provide an overview of these structures and explore applications to both calibrated and uncalibrated photometric stereo.©2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CVPRW.2009.5206731
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CVPRW.2009.5206731
dc.description.sourcetitle2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
dc.description.page2977-2984
dc.identifier.isiutNOT_IN_WOS
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