Impact of Image Blur on Classification and Augmentation of Deep Convolutional Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F23%3A00571255" target="_blank" >RIV/67985556:_____/23:00571255 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-31438-4_8" target="_blank" >http://dx.doi.org/10.1007/978-3-031-31438-4_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-31438-4_8" target="_blank" >10.1007/978-3-031-31438-4_8</a>
Alternative languages
Result language
angličtina
Original language name
Impact of Image Blur on Classification and Augmentation of Deep Convolutional Networks
Original language description
Blur is a common phenomenon in image acquisition that negatively influences the recognition rate of most classifiers. This paper studies the influence of image blurring of various types and sizes on the recognition rate achieved by a deep convolutional network. We confirm that the blur significantly decreases the performance if the network has been trained on clear images only. When the training set is augmented with blurred samples, the recognition rate becomes sufficiently high even if the blur in query images is of different size than the blur used for training. However, this is mostly not true if query images contain blur of a different type from the one used for training.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
Result continuities
Project
<a href="/en/project/GA21-03921S" target="_blank" >GA21-03921S: Inverse problems in image processing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Image Analysis: 23rd Scandinavian Conference, SCIA 2023
ISBN
978-3-031-31437-7
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
108-117
Publisher name
Springer
Place of publication
Cham
Event location
Levi
Event date
Apr 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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