Turkish Journal of Electrical Engineering and Computer Sciences




Extracting surface orientation and surface depth from one or more images is one of the classic problems in computer vision. Shape-from-shading (SFS) deals with the recovery of 3-D shape from a single shaded image. The shape is recovered by minimizing an energy functional involving constraints such as smoothness. In this constrained problem, although the smoothness constraint helps to stabilize the minimization process, it pushes the reconstruction toward a smooth surface. In this paper, we present a new adaptive shape-from-shading method which reduces this oversmoothing by controlling the smoothness spatially over the image space. In order to improve the quality of the reconstruction, we also integrated this adaptive SFS algorithm and photometric stereo technique to recover shape by using more than one input image. The new algorithm is robust, data driven and updates both the gradient field and height map simultaneously. A hierarchical implementation of the algorithm is presented. Typical SFS and photometric stereo results are given to illustrate the usefulness of our approach.


Computer vision, regularization, surface reconstruction, shape from shading

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