@TechReport{Tylecek-TR-2014-28,
  IS = { zkontrolovano 27 Jan 2015 },
  UPDATE  = { 2015-01-27 },
author =      {Tyle{\v c}ek, Radim and {\v S}{\'a}ra, Radim},
title =       {A Bayesian Approach To Multiple Reflection Symmetry Detection in Images},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2014},
month =       {December},
type =        {Research Report},
number =      {CTU--CMP--2014--28},
issn =        {1213-2365},
pages =       {17},
figures =     {7},
authorship =  {},
project =     {GACR P103/12/1578},
annote =      {We propose a method for detection of mirror-symmetric
                  objects in images, based on matching of keypoints
                  detected from covariant features and contours. A
                  set of tentative correspondences is selected from
                  all pairs of keypoints based on the similarity of
                  rotated and mirrored descriptors of local
                  appearance.  A generative probabilistic model of
                  reflection symmetry includes keypoint similarity,
                  geometric errors, and parameter priors. Symmetry
                  detection for a given input image is performed by a
                  randomized algorithm based on rigorous two-level
                  Bayesian inference, where the most probable number
                  of symmetric objects is inferred along with their
                  parameters. Results show state-of-the art
                  performance on a public benchmark dataset using an
                  inference mechanism that is not engineered for this
                  particular application.},
keywords =    {computer vision, symmetry, mirror, reflection,
                  bilateral, detection, generative modeling,
                  probabilistic modeling},
comment =     { Confidential. },
}