Mirror Symmetry Histograms for Capturing Geometric Properties in Images

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

CVPR 2014

doi:10.1109/CVPR.2014.381 /cvpr/2014/cicconet2014cvpr-mirror/

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.

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Cite

Text

Cicconet et al. "Mirror Symmetry Histograms for Capturing Geometric Properties in Images." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.381

Markdown

[Cicconet et al. "Mirror Symmetry Histograms for Capturing Geometric Properties in Images." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/cicconet2014cvpr-mirror/) doi:10.1109/CVPR.2014.381

BibTeX

@inproceedings{cicconet2014cvpr-mirror,
  title     = {{Mirror Symmetry Histograms for Capturing Geometric Properties in Images}},
  author    = {Cicconet, Marcelo and Geiger, Davi and Gunsalus, Kristin C. and Werman, Michael},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.381},
  url       = {https://mlanthology.org/cvpr/2014/cicconet2014cvpr-mirror/}
}