High dynamic range reflectance transformation imaging: an adaptive multi-light approach for visual surface quality assessment

M. Nurit; Y. Castro; A. Zendagui; G. Le Goïc; H. Favreliere; A. Mansouri

doi:10.1117/12.2521788

16 July 2019 High dynamic range reflectance transformation imaging: an adaptive multi-light approach for visual surface quality assessment

M. Nurit, Y. Castro, A. Zendagui, G. Le Goïc, H. Favreliere, A. Mansouri

Author Affiliations +

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117213 (2019) https://doi.org/10.1117/12.2521788
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

Visual inspection of surfaces is a complex sensory process that depends on several factors related to the observer [1]–[3] (his knowledge, experience, tiredness or the purpose of observation), the object observed and the light environment. In order to reduce the subjectivity and to improve the robustness of the analysis of the appearance quality of the inspected surfaces, multi-lighting imaging techniques like RTI (Reflectance Transformation Imaging) are being used more and more. However, and to make the most of this technique, it is necessary to control the acquisition and processing, especially for highly reflective surfaces and / or having particular surface topographies. Thus, we propose an innovative implementation based on the RTI technique by increasing it with an acquisition and a processing in high dynamic range (HDR), adapted and adaptive. This coupling makes it possible to avoid the low dynamic range (LDR) of the cameras and therefore ensures a faithful estimate, for each pixel, of the angular component of the reflectance of the surface inspected, in particular in the case of non-Lambertian surfaces, and heterogeneous surfaces in terms of material and / or geometric texturing. Beyond appearance reconstruction, maps associated with geometric and statistical indicator maps (local slope/kurtosis/skewness/entropy) are used to show the relevance and performance of the proposed HDR-RTI methodology.

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M. Nurit, Y. Castro, A. Zendagui, G. Le Goïc, H. Favreliere, and A. Mansouri "High dynamic range reflectance transformation imaging: an adaptive multi-light approach for visual surface quality assessment", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117213 (16 July 2019); https://doi.org/10.1117/12.2521788

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