Photometric stereo with applications in material classification

Abstract

Recovering the shape and appearance of a scene are important problems in computer vision. Of all the methods developed towards solving these problems, photometric stereo is unique in terms of estimating the fine details in the geometry of the scene based on information from shading. In this thesis, different aspects of photometric stereo are explored and newer methods are presented to increase its scope. Firstly, a method for resolving the ambiguities associated with the estimated shapes from uncalibrated Lambertian photometric stereo is presented. It is shown that a ring of light sources can explicitly determine the ambiguities and provide accurate shape estimates. Next, an algorithm for estimating depth in the case of non-Lambertian surfaces is presented, building on previous methods. Lastly, the aspects photometric stereo for reflectance based material classification are explored. Additionally, a simple handheld device is for ink classification.