Materials Classification using Sparse Gray-Scale Bidirectional Reflectance Measurements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00447072" target="_blank" >RIV/67985556:_____/15:00447072 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-23117-4_25" target="_blank" >http://dx.doi.org/10.1007/978-3-319-23117-4_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-23117-4_25" target="_blank" >10.1007/978-3-319-23117-4_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Materials Classification using Sparse Gray-Scale Bidirectional Reflectance Measurements

Popis výsledku v původním jazyce

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., dependenton 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 identifytype 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 ad

Název v anglickém jazyce

Materials Classification using Sparse Gray-Scale Bidirectional Reflectance Measurements

Popis výsledku anglicky

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., dependenton 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 identifytype 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 ad

Druh

D - Stať ve sborníku

CEP obor

BD - Teorie informace

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Analysis of Images and Patterns - CAIP 2015

ISBN

978-3-319-23192-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

289-299

Název nakladatele

Springer International Publishing

Místo vydání

Switzerland

Místo konání akce

Valletta

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000364694000025