Difference between revisions of "Publications:Illuminance Flow Estimation by Regression"

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{{PublicationSetupTemplate|Author=Stefan Karlsson, Sylvia C. Pont, Jan J. Koenderink, Andrew Zisserman
 
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|Name=Karlsson, Stefan M. [stekar] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Pont, Sylvia C. ( Pi-Lab., Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, Delft 2628 CE, Netherlands);Koenderink, Jan J. (Pi-Lab., Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, Delft 2028 CD, Netherlands);Zisserman, Andrew (Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom)
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|Name=Karlsson, Stefan M. (stekar) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Pont, Sylvia C. ( Pi-Lab., Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, Delft 2628 CE, Netherlands);Koenderink, Jan J. (Pi-Lab., Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, Delft 2028 CD, Netherlands);Zisserman, Andrew (Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom)
 
|Title=Illuminance Flow Estimation by Regression
 
|Title=Illuminance Flow Estimation by Regression
 
|PublicationType=Journal Paper
 
|PublicationType=Journal Paper

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Title Illuminance Flow Estimation by Regression
Author Stefan Karlsson and Sylvia C. Pont and Jan J. Koenderink and Andrew Zisserman
Year 2010
PublicationType Journal Paper
Journal International Journal of Computer Vision
HostPublication
Conference
DOI http://dx.doi.org/10.1007/s11263-010-0353-7
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:537591
Abstract We investigate the estimation of illuminance flow using Histograms of Oriented Gradient features (HOGs). In a regression setting, we found for both ridge regression and support vector machines, that the optimal solution shows close resemblance to the gradient based structure tensor (also known as the second moment matrix). Theoretical results are presented showing in detail how the structure tensor and the HOGs are connected. This relation will benefit computer vision tasks such as affine invariant texture/object matching using HOGs. Several properties of HOGs are presented, among others, how many bins are required for a directionality measure, and how to estimate HOGs through spatial averaging that requires no binning.