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Coniferous Trees Needles-Based Taxonomy Classification
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SYSNO ASEP 0520496 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Coniferous Trees Needles-Based Taxonomy Classification Author(s) Haindl, Michal (UTIA-B) RID, ORCID
Žid, Pavel (UTIA-B) RID, ORCIDNumber of authors 2 Source Title International Conference on Image and Vision Computing New Zealand 2019 (IVCNZ 2019). - Piscataway : IEEE, 2019 - ISSN 2151-2191 - ISBN 978-1-7281-4188-6 Pages s. 1-6 Number of pages 6 s. Publication form Print - P Action Image and Vision Computing New Zealand (IVCNZ 2019) /34./ Event date 02.12.2019 - 04.12.2019 VEvent location Dunedin Country NZ - New Zealand Event type WRD Language eng - English Country US - United States Keywords Coniferous needles categorization ; Tree taxonomy recognition ; Spiral Markov random field model Subject RIV BD - Theory of Information OECD category Automation and control systems R&D Projects GA19-12340S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 EID SCOPUS 85078695401 DOI 10.1109/IVCNZ48456.2019.8961023 Annotation This paper introduces multispectral rotationally invariant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2020
Number of the records: 1