Mirror symmetry histograms for capturing geometric properties in images

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages2981-2986
Number of pages6
ISBN (Print)9781479951178, 9781479951178
DOIs
StatePublished - Sep 24 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: Jun 23 2014Jun 28 2014

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period6/23/146/28/14

Fingerprint

Mirrors
Data structures
Pixels

Keywords

  • biology
  • cell
  • circle fitting
  • geometric representation
  • histogram
  • hough transform
  • mirror symmetry

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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). [6909777] IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.381

Mirror symmetry histograms for capturing geometric properties in images. / Cicconet, Marcelo; Geiger, Davi; Gunsalus, Kristin C.; Werman, Michael.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 2981-2986 6909777.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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, IEEE Computer Society, pp. 2981-2986, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 6/23/14. https://doi.org/10.1109/CVPR.2014.381
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 https://doi.org/10.1109/CVPR.2014.381
Cicconet, Marcelo ; Geiger, Davi ; Gunsalus, Kristin C. ; Werman, Michael. / 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
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