@inproceedings{Pinheiro-ICIP2015,
  IS = { zkontrolovano 07 Jan 2016 },
UPDATE   = { 2015-12-26 },
author      = {Am{\' a}vel Pinheiro, Miguel and Kybic, Jan},
affiliation = {13133-13133},
authorship  = {50-50},
Title       = {{Geometrical Graph Matching using Monte Carlo Tree Search}},
booktitle   = {Proceedings of the IEEE International Conference in Image Processing (ICIP)},
pages    = { 3145--3149 },
book_pages = { 4986 },
isbn        = { 978-1-4799-8339-1 },
publisher = { IEEE Computer Society },
address  = { Los Alamitos, USA },
month       = { Septemper },
day         = { 27--30 },
Year        = { 2015 },
venue       = { Qu{\' e}bec City, Canada },
keywords    = {graph matching, image registration, Monte Carlo Tree
                  Search, path descriptor},
annote      = {Many medical images contain graph-like geometrical
    structures such as blood vessels and neuronal networks. We 
    present an algorithm for matching geometrical graphs, in order 
    to quickly and robustly align such images. We use a sampling-based 
    curve descriptor to prune dissimilar edges. The matching is 
    modeled as a single-player game, growing the matching from a 
    random initial correspondence. The coarse global solution is 
    found using a Monte Carlo Tree Search and then refined locally.
    We show experimentally that our approach finds the correct matching 
    in all tested datasets and is the fastest of all global methods.},
Project     = {SGS15/156/OHK3/2T/13, GACR 14-21421S, SFRH/BD/77134/2011},
Psurl       = {[Pinheiro-ICIP2015.pdf]},
Url         = {ftp://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Pinheiro-ICIP2015.pdf},
doi         = { 10.1109/ICIP.2015.7351383 },
acceptance_ratio = { 45 },
}