Solving the Uncalibrated Photometric Stereo Problem using Total Variation

International audienceIn this paper we propose a new method to solve the problem of uncalibrated photometric stereo, making very weak assumptions on the properties of the scene to be reconstructed. Our goal is to solve the generalized bas-relief ambiguity (GBR) by performing a total variation regularization of both the estimated normal field and albedo. Unlike most of the previous attempts to solve this ambiguity, our approach does not rely on any prior information about the shape or the albedo, apart from its piecewise smoothness. We test our method on real images and obtain results comparable to the state-of-the-art algorithms

Practice-Based Comparison of Imaging Methods for Visualization of Toolmarks on an Egyptian Scarab

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Characterization of Human Faces under Illumination Variations Using Rank, Integrability, and Symmetry Constraints

Photometric stereo algorithms use a Lambertian reflectance model with a varying albedo field and involve the appearances of only one object. This paper extends photometric stereo algorithms to handle all the appearances of all the objects in a class, in particular the class of human faces. Similarity among all facial appearances motivates a rank constraint on the albedos and surface normals in the class. This leads to a factorization of an observation matrix that consists of exemplar images of di#erent objects under di#erent illuminations, which is beyond what can be analyzed using bilinear analysis. Bilinear analysis requires exemplar images of di#erent objects under same illuminations. To fully recover the class-specific albedos and surface normals, integrability and face symmetry constraints are employed. The proposed linear algorithm takes into account the e#ects of the varying albedo field by approximating the integrability terms using only the surface normals. As an application, face recognition under illumination variation is presented