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An Invariant and Compact Representation for Unrestricted Pose Estimation
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
2005 (English)In: Second Iberian Conference Pattern Recognition and Image Analysis (IbPRIA), Berlin / Heidelberg: Springer , 2005, Vol. 3522Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes a novel compact representation of local features called the tensor doublet. The representation generates a four dimensional feature vector which is significantly less complex than other approaches, such as Lowe's 128 dimensional feature vector. Despite its low dimensionality, we demonstrate here that the tensor doublet can be used for pose estimation, where the system is trained for an object and evaluated on images with cluttered background and occlusion.

Place, publisher, year, edition, pages
Berlin / Heidelberg: Springer , 2005. Vol. 3522
Series
LNCS, ISSN 0302-9743 (Print) 1611-3349 (Online) ; 3522
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-48198DOI: 10.1007/b136825ISBN: 978-3-540-26153-7 (print)OAI: oai:DiVA.org:liu-48198DiVA: diva2:269094
Conference
Second Iberian Conference Pattern Recognition and Image Analysis
Projects
VISATEC, VISCOS
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2009-11-26

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Söderberg, RobertNordberg, KlasGranlund, Gösta

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