Mirror symmetry histograms for capturing geometric properties in images

Marcelo Cicconet; Davi Geiger; Kristin C. Gunsalus; Michael Werman

doi:10.1109/CVPR.2014.381

Mirror symmetry histograms for capturing geometric properties in images

Marcelo Cicconet^*, Davi Geiger, Kristin C. Gunsalus, 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

19 Scopus citations

Abstract

We propose a data structure that captures global geometric properties in images: Histogram of Mirror Symmetry Coefficients. We compute such a coefficient for every pair of pixels, and group them in a 6-dimensional histogram. By marginalizing the HMSC in various ways, we develop algorithms for a range of applications: detection of nearly-circular cells, location of the main axis of reflection symmetry, detection of cell-division in movies of developing embryos, detection of worm-tips and indirect cell-counting via supervised classification. Our approach generalizes a series of histogram-related methods, and the proposed algorithms perform with state-of-the-art accuracy.

Original language	English
Title of host publication	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher	IEEE Computer Society
Pages	2981-2986
Number of pages	6
ISBN (Electronic)	9781479951178, 9781479951178
DOIs	https://doi.org/10.1109/CVPR.2014.381
State	Published - 24 Sep 2014
Event	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014

Publication series

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

Conference

Conference	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/Territory	United States
City	Columbus
Period	23/06/14 → 28/06/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

biology
cell
circle fitting
geometric representation
histogram
hough transform
mirror symmetry

Access to Document

10.1109/CVPR.2014.381

Cite this

Cicconet, M., Geiger, D., Gunsalus, K. C., & Werman, M. (2014). Mirror symmetry histograms for capturing geometric properties in images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2981-2986). Article 6909777 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.381

Cicconet, Marcelo ; Geiger, Davi ; Gunsalus, Kristin C. et al. / Mirror symmetry histograms for capturing geometric properties in images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. pp. 2981-2986 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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Cicconet, M, Geiger, D, Gunsalus, KC & Werman, M 2014, Mirror symmetry histograms for capturing geometric properties in images. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909777, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp. 2981-2986, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 23/06/14. https://doi.org/10.1109/CVPR.2014.381

Mirror symmetry histograms for capturing geometric properties in images. / Cicconet, Marcelo; Geiger, Davi; Gunsalus, Kristin C. et al.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 2981-2986 6909777 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

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

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Cicconet M, Geiger D, Gunsalus KC, Werman M. Mirror symmetry histograms for capturing geometric properties in images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. 2014. p. 2981-2986. 6909777. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). doi: 10.1109/CVPR.2014.381

Mirror symmetry histograms for capturing geometric properties in images

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Keywords

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