Convolutional neural network architecture for geometric matching
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00329435" target="_blank" >RIV/68407700:21730/19:00329435 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2018.2865351" target="_blank" >https://doi.org/10.1109/TPAMI.2018.2865351</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2018.2865351" target="_blank" >10.1109/TPAMI.2018.2865351</a>
Alternative languages
Result language
angličtina
Original language name
Convolutional neural network architecture for geometric matching
Original language description
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine, homography or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging PF, TSS and Caltech-101 datasets. IEEE
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
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
1939-3539
Volume of the periodical
41
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
2553-2567
UT code for WoS article
000489838200002
EID of the result in the Scopus database
2-s2.0-85051638908