Affine invariance revisited

Evgeni Begelfor; Michael Werman

doi:10.1109/CVPR.2006.50

Affine invariance revisited

Evgeni Begelfor^*, Michael Werman

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

111 Scopus citations

Abstract

This paper proposes a Riemannian geometric framework to compute averages and distributions of point configurations so that different configurations up to affine transformations are considered to be the same. The algorithms are fast and proven to be robust both theoretically and empirically. The utility of this framework is shown in a number of affine invariant clustering algorithms on image point data.

Original language	American English
Title of host publication	Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages	2087-2094
Number of pages	8
DOIs	https://doi.org/10.1109/CVPR.2006.50
State	Published - 2006
Event	2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States Duration: 17 Jun 2006 → 22 Jun 2006

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2
ISSN (Print)	1063-6919

Conference

Conference	2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Country/Territory	United States
City	New York, NY
Period	17/06/06 → 22/06/06

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10.1109/CVPR.2006.50

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title = "Affine invariance revisited",

abstract = "This paper proposes a Riemannian geometric framework to compute averages and distributions of point configurations so that different configurations up to affine transformations are considered to be the same. The algorithms are fast and proven to be robust both theoretically and empirically. The utility of this framework is shown in a number of affine invariant clustering algorithms on image point data.",

author = "Evgeni Begelfor and Michael Werman",

year = "2006",

doi = "10.1109/CVPR.2006.50",

language = "American English",

isbn = "0769525970",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

pages = "2087--2094",

booktitle = "Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006",

note = "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 ; Conference date: 17-06-2006 Through 22-06-2006",

}

Begelfor, E & Werman, M 2006, Affine invariance revisited. in Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006., 1641009, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2087-2094, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, New York, NY, United States, 17/06/06. https://doi.org/10.1109/CVPR.2006.50

Affine invariance revisited. / Begelfor, Evgeni; Werman, Michael.
Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006. 2006. p. 2087-2094 1641009 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper proposes a Riemannian geometric framework to compute averages and distributions of point configurations so that different configurations up to affine transformations are considered to be the same. The algorithms are fast and proven to be robust both theoretically and empirically. The utility of this framework is shown in a number of affine invariant clustering algorithms on image point data.

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T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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Affine invariance revisited

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