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Fast segmentation of sparse 3D point trajectories using group theoretical invariants
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0001-7557-4904
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.ORCID iD: 0000-0002-6096-3648
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2015 (English)In: COMPUTER VISION - ACCV 2014, PT IV / [ed] D. Cremers, I. Reid, H. Saito, M.-H. Yang, Springer, 2015, Vol. 9006, 675-691 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel approach for segmenting different motions from 3D trajectories. Our approach uses the theory of transformation groups to derive a set of invariants of 3D points located on the same rigid object. These invariants are inexpensive to calculate, involving primarily QR factorizations of small matrices. The invariants are easily converted into a set of robust motion affinities and with the use of a local sampling scheme and spectral clustering, they can be incorporated into a highly efficient motion segmentation algorithm. We have also captured a new multi-object 3D motion dataset, on which we have evaluated our approach, and compared against state-of-the-art competing methods from literature. Our results show that our approach outperforms all methods while being robust to perspective distortions and degenerate configurations.

Place, publisher, year, edition, pages
Springer, 2015. Vol. 9006, 675-691 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9006
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-114313DOI: 10.1007/978-3-319-16817-3_44ISI: 000362444500044ISBN: 978-3-31916-816-6 (print)ISBN: 978-3-31916-817-3 (print)OAI: oai:DiVA.org:liu-114313DiVA: diva2:789181
Conference
12th Asian Conference on Computer Vision (ACCV) Singapore, Singapore, November 1-5 2014
Projects
VPSCUASETT
Available from: 2015-02-18 Created: 2015-02-18 Last updated: 2016-08-31

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Zografos, VasileiosLenz, ReinerRingaby, ErikFelsberg, MichaelNordberg, Klas

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Zografos, VasileiosLenz, ReinerRingaby, ErikFelsberg, MichaelNordberg, Klas
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