1 May 2003 Estimation of the luminance map for Lambertian photometric model: application to the study of a road surface roughness
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Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.514955
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States
Abstract
The study of rough textured surface as road coverings, are generally made on grey level images. This suppose, that the variations of grey levels are representative of the local variations of the relief. This justified assumption, in the case of surfaces uniformly colored, finds its limit in the case when these surfaces present variations of color or aspect. The corresponding image will then present variations of grey levels which can be related to the color variations or to the relief variations or both. It then becomes difficult in this case to work out criteria of roughness based on image analysis. It is then necessary to work out, before any study of roughness, an estimation of the luminance map linked to color variations. In order to do that, we have linked the grey level value to the height variations, obtained through a laser sensor, and to the image grey level. The suggested method allows us to compute the distribution of the luminance map. We characterize this distribution by considering statistical parameters of its histogram. We have tested the effectiveness of our approach by comparing the evolutions of the criteria of roughness on road surfaces, without considering the luminance distribution and by taking it into account. The results obtained show that the developed approach leads to a good discrimination by the criteria of roughness in the case of colored surfaces.
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Khoudeir Majdi, Khoudeir Majdi, Brochard Jacques, Brochard Jacques, Min-Tan Do, Min-Tan Do, Vincent Legeay, Vincent Legeay, } "Estimation of the luminance map for Lambertian photometric model: application to the study of a road surface roughness", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.514955; https://doi.org/10.1117/12.514955
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