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Materials Classification using Sparse Gray-Scale Bidirectional Reflectance Measurements
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SYSNO ASEP 0447072 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Materials Classification using Sparse Gray-Scale Bidirectional Reflectance Measurements Author(s) Filip, Jiří (UTIA-B) RID, ORCID
Somol, Petr (UTIA-B) RIDNumber of authors 2 Source Title Computer Analysis of Images and Patterns - CAIP 2015, I. - Switzerland : Springer International Publishing, 2015 / Azzopardi George ; Petkov Nicolai - ISSN 0302-9743 - ISBN 978-3-319-23192-1 Pages s. 289-299 Number of pages 11 s. Publication form Medium - C Action 16th International Conference on Computer Analysis of Images and Patterns Event date 02.09.2015-04.09.2015 VEvent location Valletta Country MT - Malta Event type WRD Language eng - English Country DE - Germany Keywords BRDF ; material ; classification ; feature selection Subject RIV BD - Theory of Information R&D Projects GA14-02652S GA ČR - Czech Science Foundation (CSF) GA14-10911S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000364694000025 EID SCOPUS 84945971286 DOI https://doi.org/10.1007/978-3-319-23117-4_25 Annotation Material recognition applications use typically color texture-based features; however, the underlying measurements are in several application fields unavailable or too expensive. Therefore, bidirectional reflectance measurements are used, i.e., dependent on both illumination and viewing directions. But even measurement of such BRDF data is very time- and resources-demanding. In this paper we use dependency-aware feature selection method to identify very sparse set of the most discriminative bidirectional reflectance samples that can reliably distinguish between three types of materials from BRDF database - fabric, wood, and leather. We conclude that ten gray-scale samples primarily at high illumination and viewing elevations are sufficient to identify type of material with accuracy over 96/%. We analyze estimated placement of the bidirectional samples for discrimination between different types of materials. The stability of such directional samples is very high as was verified by an additional leave-one-out classification experiment. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2016
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