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• W1967339296 abstract "This paper addresses the issue of regularization in the surface reconstruction from gradients problem in Industrial Photometric Stereo. Regularization of the solution is a necessary step in an industrial environment, where algorithms must cope with non-Gaussian noise, such as outliers, or non-Lambertian textures such as corrosion. Introducing Tikhonov regularization into the global least squares solution suppresses the influence of outliers in the reconstruction. Viable methods should both minimize a global least squares cost function and also introduce some form of regularization into the solution; state-of-the-art methods to this end are grossly inefficient and are severely limited in the size of surface they can reconstruct. We present a new algorithm which can reconstruct a surface of 1200x1200, (i.e., greater than 1M-pixel) in a few seconds. This is orders of magnitude faster than state-of-the-art methods incorporating regularization, and hence presents the first method viable for regularized reconstructions in practical applications." @default.
• W1967339296 created "2016-06-24" @default.
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• W1967339296 date "2013-12-01" @default.
• W1967339296 modified "2023-10-16" @default.
• W1967339296 title "Direct regularized surface reconstruction from gradients for Industrial Photometric Stereo" @default.
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• W1967339296 doi "https://doi.org/10.1016/j.compind.2013.03.013" @default.
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