Rotationally Invariant Bark Recognition

0492498 - ÚTIA 2019 RIV CH eng C - Conference Paper (international conference)
Remeš, Václav - Haindl, Michal
Rotationally Invariant Bark Recognition.
Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, S+SSPR 2018. Cham: Springer Nature Switzerland AG, 2018 - (Bai, X.; Hancock, E.; Ho, T.; Wilson, R.; Biggio, B.; Robles-Kelly, A.), s. 22-31, č. článku 3. Lecture Notes in Computer Science, 11004. ISBN 978-3-319-97784-3. ISSN 0302-9743.
[IAPR Joint International Workshop on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition. Beijing (CN), 17.08.2018-19.08.2018]
Institutional support: RVO:67985556
Keywords : Bark recognition * Tree taxonomy clasification * Spiral Markov random field model
OECD category: Automation and control systems
http://library.utia.cas.cz/separaty/2018/RO/haindl-0492498.pdf

An efficient bark recognition method based on a novel wide-sense Markov spiral model textural representation is presented. Unlike the alternative bark recognition methods based on various gray-scale discriminative textural descriptions, we benefit from fully descriptive color, rotationally invariant bark texture representation. The proposed method significantly outperforms the state-of-the-art bark recognition approaches in terms of the classification accuracy.
Permanent Link: http://hdl.handle.net/11104/0286554