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View Dependent Surface Material Recognition
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SYSNO ASEP 0510488 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title View Dependent Surface Material Recognition Author(s) Mikeš, Stanislav (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDNumber of authors 2 Article number 12 Source Title Advances in Visual Computing : 14th International Symposium on Visual Computing (ISVC 2019). - Cham : Springer, 2019 / Bebis G. ; Boyle R. ; Parvin B. ; Koracin D. - ISSN 0302-9743 - ISBN 978-3-030-33719-3 Pages s. 156-167 Number of pages 12 s. Publication form Print - P Action International Symposium on Visual Computing (ISVC 2019) /14./ Event date 07.10.2019 - 09.10.2019 VEvent location Lake Tahoe Country US - United States Event type WRD Language eng - English Country CH - Switzerland Keywords convolutional neural network ; texture recognition ; Bidirectional Texture Function recognition 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 UT WOS 000582481300012 EID SCOPUS 85076168125 DOI 10.1007/978-3-030-33720-9_12 Annotation The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation. 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