IS = { zkontrolovano 12 Jan 2012 },
  UPDATE  = { 2011-10-17 },
author =      {Ma{\v c}{\'a}k, Jan},
supervisor =  {Drbohlav, Ond{\v r}ej and {\v S}{\'a}ra, Radim},
title =       {Hierarchical and Syntactic Shape Description and 
               Recognition -- {PhD} Thesis Proposal},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2011},
month =       {September},
day =         {14},
type =        {Research Report},
number =      {CTU--CMP--2011--10},
issn =        {1213-2365},
pages       = {32},
figures     = {10},
authorship =  {100},
psurl       = {[Macak-TR-2011-10.pdf]},
project =     {SGS10/278/OHK3/3T/13},
annote =      {Classification and detection of objects from many
    classes in images is a classical problem in computer vision. One
    of possible approaches is to work with object shape which is
    robust to illumination changes, colour changes and also quite
    robust to small view-point changes. The modelling of object shapes
    in a hierarchical manner and using this models for detection and
    classification of objects in images is a very promising area of
    research since it scales well with an increasing number of classes
    which one would like to recognize. In fact, thanks to possible
    sharing of hierarchy components, a sub-linear method for
    classification could be designed. Although the underlying
    hierarchical model has almost stabilised, it is not still clear
    how to efficiently learn parameters of such models and how to
    efficiently infer class instances in novel images using this sort
    of models. This work summarizes the current state-of-the-art
    methods in this field, proposes some improvements and identifies
    possible directions of research. },
keywords =    {hierarchical object representation, object detection, 
    object classification},