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Proceedings Paper

Light spatial distribution calibration based on local density estimation for reflectance transformation imaging
Author(s): Yuly Castro; Gilles Pitard; Abir Zendagui; Gaëtan Le Göic; Vincent Brost; Arnaud Boucher; Alamin Mansouri
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Paper Abstract

Reflectance Transformation Imaging (RTI) is a multi-light-based imaging technique that can provide relevant information on both local micro-geometry and visual appearance of a studied surface. The local angular reflectance is modelled to allow the relighting of the surface appearance under any arbitrary light direction. The methods used to model the local reflectance of each pixel are mainly PTM (2nd order polynomial functions), HSH (Hemispherical Harmonics) and more recently DMD (Dissrete Modal Decomposition). For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it’s impossible to satisfy this condition in practice. As a result of this non-homogeneous distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we proposed a methodology consisting in the estimation of the spatial distribution of the lighting directions used during RTI acquisitions, based on a local density estimation. These local density values are then used to weight the Least Squares regression, and thus to correct the contributions of each image of the RTI acquisitions. This methodology is applied on two metallic surfaces with visual singularities. From presented results, it can be concluded that it is necessary to take into account this non-uniformity in order not to alter the quality of RTI data and subsequent inspection tasks.

Paper Details

Date Published: 16 July 2019
PDF: 9 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720A (16 July 2019); doi: 10.1117/12.2521849
Show Author Affiliations
Yuly Castro, Univ. de Bourgogne (France)
Gilles Pitard, Saphir SAS (France)
Abir Zendagui, Univ. de Bourgogne (France)
Gaëtan Le Göic, Univ. de Bourgogne (France)
Vincent Brost, Univ. de Bourgogne (France)
Arnaud Boucher, Univ. de Bourgogne (France)
Alamin Mansouri, Univ. de Bourgogne (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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