Paper Info
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.
Year
DOI
Venue
2014
10.1109/CVPR.2014.381
CVPR
Keywords
Field
DocType
image classification,object detection,6-dimensional histogram,HMSC,cell-division detection,data structure,global geometric properties,histogram-related methods,mirror symmetry coefficient histograms,nearly-circular cell detection,reflection symmetry,supervised classification,worm-tips detection,biology,cell,circle fitting,geometric representation,histogram,hough transform,mirror symmetry
Reflection symmetry,Histogram,Data structure,Computer vision,Pattern recognition,Computer science,Histogram matching,Hough transform,Mirror symmetry,Artificial intelligence,Pixel,Image histogram
Conference
ISSN
Citations 
PageRank 
1063-6919
6
0.47
References 
Authors
21
4
Name
Order
Citations
PageRank
Marcelo Cicconet1287.08
Davi Geiger21050353.66
Kristin C. Gunsalus391.19
M. Werman4343112.04