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11 January 2020 Calibration of spatial distribution of light sources in reflectance transformation imaging based on adaptive local density estimation
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Reflectance transformation imaging (RTI) is a multilight-based imaging technique that can provide relevant information on both local microgeometry and visual appearance of a studied surface. The pixelwise angular reflectance is modeled to allow the relighting of the surface appearance under any arbitrary light direction. The primary methods used to model this local reflectance in each pixel are polynomial texture mapping, hemispherical harmonics, and, more recently, discrete modal decomposition. For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it is not always possible to satisfy this condition in practice. As a result of this nonuniform distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we propose a methodology consisting of 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 used afterward to weight the least square regression and thus correct the contributions of each image to the RTI acquisition. This methodology is applied on three surfaces with visual singularities, which present different reflectance responses. From the presented results, it can be concluded that it is necessary to take into account this nonuniformity in order not to alter the quality of reconstruction/relighting from RTI data and the subsequent inspection task.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00 © 2020 SPIE and IS&T
Yuly Castro, Marvin Nurit, Gilles Pitard, Abir Zendagui, Gaëtan Le Goïc, Vincent Brost, Arnaud Boucher, Alamin Mansouri, Anthony Pamart, and Livio De Luca "Calibration of spatial distribution of light sources in reflectance transformation imaging based on adaptive local density estimation," Journal of Electronic Imaging 29(4), 041004 (11 January 2020).
Received: 1 October 2019; Accepted: 13 December 2019; Published: 11 January 2020

Cited by 4 scholarly publications.
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