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Bark recognition using novel rotationally invariant multispectral textural features
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SYSNO ASEP 0506602 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Bark recognition using novel rotationally invariant multispectral textural features Author(s) Remeš, Václav (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Pattern Recognition Letters. - : Elsevier - ISSN 0167-8655
Roč. 125, č. 1 (2019), s. 612-617Number of pages 6 s. Publication form Print - P Language eng - English Country NL - Netherlands Keywords Bark recognition ; Tree taxonomy clasification ; Spiral Markov random field model ; textural feature Subject RIV BD - Theory of Information OECD category Communication engineering and systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000482374500084 EID SCOPUS 85068558335 DOI 10.1016/j.patrec.2019.06.027 Annotation We present novel rotationally invariant fully multispectral Markovian textural features applied for the efficient tree bark recognition. These textural features are derived from the novel descriptive multispectral spiral wide-sense Markov model. 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 methods significantly outperform the state-of-the-art bark recognition approaches regarding classification accuracy. Both our classifiers outperform convolutional neural network ResNet even on the largest public bark database BarkNet which contains 23 000 high-resolution images from 23 different tree species. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020 Electronic address https://www.sciencedirect.com/science/article/pii/S0167865519301886
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