@TechReport{Sara-TR-2014-29,
  IS = { zkontrolovano 28 Jan 2015 },
  UPDATE  = { 2015-01-28 },
author =      {{\v S}{\'a}ra, Radim},
affiliation = {13133},
title =       {A General Two-Level Bayesian Inference Solver for The
                  Detection of An Unknown Number of Objects},
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--29},
issn =        {1213-2365},
pages =       {39},
figures =     {8},
authorship =  {100},
project =     {GACR P103/12/1578},
annote =      { This report describes a generic two-level inference
                  algorithm which can be used to detect the presence
                  of an unknown number of instances of a model in
                  data. Overfitting is avoided by posing the inference
                  as a Bayesian model selection problem. Application
                  of the algorithm to several simple problems
                  demonstrates several possible ways how to construct
                  a probabilistic model. This report gives an analysis
                  of all important design choices and all aspects of
                  the approach and serves mostly as a documentation of
                  the theoretical results used in the inference engine
                  implementation.},
keywords =    {computer vision, object detection, model selection},
comment =     {Confidential. Non-public version for project review.},
}