Image contours detection with deep features and SVM
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F18%3AA1901NAY" target="_blank" >RIV/61988987:17610/18:A1901NAY - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-66824-6_48" target="_blank" >http://dx.doi.org/10.1007/978-3-319-66824-6_48</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-66824-6_48" target="_blank" >10.1007/978-3-319-66824-6_48</a>
Alternative languages
Result language
angličtina
Original language name
Image contours detection with deep features and SVM
Original language description
This contribution introduces the image contours detection based on the features extracted by a deep convolutional neural network. Popular pre-trained network VGG19 was used to extract 5504 different features for each input image pixel and then classified by a neural network with SVM classifier.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Fuzzy Logic and Technology 2017. Proc. EUSFLAT-2017 (Book series: Advances in Intelligent Systems and Computing)
ISBN
978-331966823-9
ISSN
2194-5357
e-ISSN
—
Number of pages
8
Pages from-to
546-553
Publisher name
Springer Verlag
Place of publication
Berlin
Event location
Warsaw
Event date
Sep 11, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000432807900048