Mirror Symmetry Histograms for Capturing Geometric Properties in Images

Computer Vision and Pattern Recognition, 2014

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.

Link to Paper


  author        = {M. Cicconet and K. Gunsalus and D. Geiger and M. Werman},
  title         = {Mirror Symmetry Histograms
                   for Capturing Geometric Properties in Images},
  howpublished  = {CVPR},
  year          = {2014},
  note          = {Columbus, Ohio}

Code: C++ (cross platform), C++ (Mac), Matlab.