Structure tensor: Difference between revisions

Revision as of 20:48, 29 August 2019

Revision as of 10:10, 27 February 2020

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J. Bigun and G. Granlund (1986), ''Optimal Orientation Detection of Linear Symmetry''. Tech. Report LiTH-ISY-I-0828, Computer Vision Laboratory, Linkoping University, Sweden 1986; Thesis Report, Linkoping studies in science and technology No. 85, 1986.

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{{cite conference|author1=J. Bigun |author2=G. Granlund |lastauthoramp=yes |title=Optimal Orientation Detection of Linear Symmetry|___location=Piscataway|booktitle=First int. Conf. on Computer Vision, ICCV, (London) |publisher=IEEE Computer Society Press, Piscataway|pages=433–438|year=1987 }}

</ref><ref name=knutsson89>

{{cite conference|author=H. Knutsson|title=Representing local structure using tensors|___location=Oulu|booktitle=Proceedings 6th Scandinavian Conf. on Image Analysis|publisher=Oulu University|pages=244–251|year=1989}}

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|booktitle=Proc. European Conference on Computer Vision|volume=4|pages=100–113|year=2004}}

</ref><ref>

{{cite conference|author1=C. Kenney, M. Zuliani |author2=B. Manjunath, |lastauthoramp=yes |title=An Axiomatic Approach to Corner Detection|booktitle=Proc. IEEE Computer Vision and Pattern Recognition|pages=191–197|year=2005}}

</ref> The structure tensor also plays a central role in the [[Lucas–Kanade Optical Flow Method|Lucas-Kanade optical flow algorithm]], and in its extensions to estimate [[affine shape adaptation]];<ref name=lingar97/> where the magnitude of <math>\lambda_2</math> is an indicator of the reliability of the computed result. The tensor has been used for [[scale space]] analysis,<ref name=lin94book/> estimation of local surface orientation from monocular or binocular cues,<ref name=garlin96/> non-linear [[fingerprint enhancement]],<ref>

A. Almansa and T. Lindeberg (2000), ''[http://www.nada.kth.se/cvap/abstracts/cvap226.html Enhancement of fingerprint images using shape-adaptated scale-space operators]''. IEEE Transactions on Image Processing, volume 9, number 12, pages 2027–2042.

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</ref> corresponding to the transfer of [[affine shape adaptation]]<ref name=lingar97/> from spatial to spatio-temporal image data.

In combination with local spatio-temporal histogram descriptors,<ref>

{{cite conference|author1=I. Laptev |author2=T. Lindeberg |last-author-amp=yes |title=Local descriptors for spatio-temporal recognition|booktitle=ECCV'04 Workshop on Spatial Coherence for Visual Motion Analysis (Prague, Czech Republic) Springer Lecture Notes in Computer Science|url=http://www.csc.kth.se/cvap/abstracts/LapLin04-ECCVWS.html|doi=10.1007/11676959|date=May 2004|volume=3667| pages=91–103.}}

</ref>

these concepts together allow for Galilean invariant recognition of spatio-temporal events.<ref>