@inproceedings{451c8b6bd9504aeab9cb7b3003d5ce7e,
title = "Mirror symmetry histograms for capturing geometric properties in images",
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.",
keywords = "biology, cell, circle fitting, geometric representation, histogram, hough transform, mirror symmetry",
author = "Marcelo Cicconet and Davi Geiger and Gunsalus, \{Kristin C.\} and Michael Werman",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 ; Conference date: 23-06-2014 Through 28-06-2014",
year = "2014",
month = sep,
day = "24",
doi = "10.1109/CVPR.2014.381",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "2981--2986",
booktitle = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
address = "الولايات المتّحدة",
}