| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0571255 |
| utime |
20240402213835.2 |
| mtime |
20230428235959.9 |
| SCOPUS |
85160816520 |
| DOI |
10.1007/978-3-031-31438-4_8 |
| title
(primary) (eng) |
Impact of Image Blur on Classification and Augmentation of Deep Convolutional Networks |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0571254 |
| ISBN |
978-3-031-31437-7 |
| title
|
Image Analysis: 23rd Scandinavian Conference, SCIA 2023 |
| page_num |
108-117 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2023 |
|
| editor |
|
|
| keyword |
Image recognition |
| keyword |
Blur |
| keyword |
Augmentation of the training set |
| keyword |
Convolutional neural network |
| author
(primary) |
| ARLID |
cav_un_auth*0377447 |
| name1 |
Lébl |
| name2 |
Matěj |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept (eng) |
Department of Image Processing |
| department (cz) |
ZOI |
| department (eng) |
ZOI |
| full_dept |
Department of Image Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101209 |
| name1 |
Šroubek |
| name2 |
Filip |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101087 |
| name1 |
Flusser |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování obrazové informace |
| full_dept |
Department of Image Processing |
| department (cz) |
ZOI |
| department |
ZOI |
| full_dept |
Department of Image Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-03921S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0412209 |
|
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0449343 |
| name |
Scandinavian Conference on Image Analysis 2023 /23./ |
| dates |
20230418 |
| mrcbC20-s |
20230421 |
| place |
Levi |
| country |
FI |
|
| RIV |
JD |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20206 |
| reportyear |
2024 |
| num_of_auth |
3 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0342934 |
| confidential |
S |
| arlyear |
2023 |
| mrcbU14 |
85160816520 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0571254 Image Analysis: 23rd Scandinavian Conference, SCIA 2023 978-3-031-31437-7 108 117 Cham Springer 2023 Lecture notes on computer science LNCS 13886 |
| mrcbU67 |
Gade R. 340 |
|