Publ3 search

Z6_60GI02O0O8IDC0QEJUJ26TJDI4

Error:

Javascript is disabled in this browser. This page requires Javascript. Modify your browser's settings to allow Javascript to execute. See your browser's documentation for specific instructions.

Publikační činnost

Probíhá načítání, čekejte prosím...

Record type:	stať ve sborníku (D)
Home Department:	Ústav pro výzkum a aplikace fuzzy modelování (94410)
Title:	Fuzzy transform and support vector machine for pedestrian detection
Citace	Vlašánek, P. a Stuchlík, L. Fuzzy transform and support vector machine for pedestrian detection. In: ICIEV: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) 2018-06-25 Kitakyushu, Japonsko. IEEE, 2018. s. 344-348. ISBN 978-1-5386-5163-6.

Subtitle
Publication year:	2018
Obor:	Obecná matematika
Number of pages:	5
Page from:	344
Page to:	348
Form of publication:	Elektronická verze
ISBN code:	978-1-5386-5163-6
ISSN code:
Proceedings title:	2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)
Proceedings:	Mezinárodní
Publisher name:	IEEE
Place of publishing:	neuvedeno
Country of Publication:
Název konference:	ICIEV
Místo konání konference:	Kitakyushu, Japonsko
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Celosvětová akce
WoS code:	000462610300060
EID:	2-s2.0-85063194316

Key words in English:	F-transform;image processing;pedestrian detection;svm
Annotation in original language:	This paper focuses on a task of automatic pedestrian detection. There are many possible solutions with several ideas behind. Histogram of oriented gradients is among the popular ones. The former is used as a descriptor of the input image together with support vector machine. In this initial study, we propose to create a feature vector using fuzzy mathematics and test its effectiveness. The effectiveness is evaluated by comparison with original histogram of oriented gradient descriptors in support vector machine.
Annotation in english language:

References

Reference

R01:	RIV/61988987:17610/18:A2001TSW

Complementary Content

${loading}