Non-Rigid Graph Registration using Active Testing Search
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00226745" target="_blank" >RIV/68407700:21230/15:00226745 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Serradell-PAMI2015.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/amavemig/Serradell-PAMI2015.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2014.2343235" target="_blank" >10.1109/TPAMI.2014.2343235</a>
Alternative languages
Result language
angličtina
Original language name
Non-Rigid Graph Registration using Active Testing Search
Original language description
We present a new approach for matching sets of branching curvilinear structure s that form graphs embedded in $mathbb{R}^2$ or $mathbb{R}^3$ and may be subject to deformations. Unlike earlier method s, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformatio ns, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian Processes to represen t the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches consider ed at each iteration is not selected at random as in many RANSAC-based algorithms. Instead, we introduce a so-called Active Testin g Search
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP202%2F11%2F0111" target="_blank" >GAP202/11/0111: Automatic analysis of light and electron microscopy neuronal data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
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Volume of the periodical
37
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
625-638
UT code for WoS article
000349626200011
EID of the result in the Scopus database
2-s2.0-84923013613