Improved methods of estimating shape from shading using the light source coordinate system
Artificial Intelligence
On the Imaging of Fractal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
Height and gradient from shading
International Journal of Computer Vision
Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal Geometry in Digital Imaging
Fractal Geometry in Digital Imaging
Shape from Shading: A Well-Posed Problem?
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Wavelets, fractals, and radial basis functions
IEEE Transactions on Signal Processing
A fractal-based relaxation algorithm for shape from terrain image
Computer Vision and Image Understanding
| Hi-index | 0.00 |
We consider the generalized regularization problem of Shape-from- Shading. The traditional algorithms are to find the minimum point of the optimization problem where the regularization term is considered as the part of the objective function. However, the result of regularization may deviate from the true surface, due to the ill-posedness of the SFS problem. In this paper, we propose a novel method to solve this problem. The algorithm consists of two steps. In the first step, we recover the components of the surface in the range space of the transpose of the system matrix, from the observed image by using the Landweber iteration method, where the Pentland's linear SFS model is adopted without any regularization. In the second step, we represent the regularization condition as an energy spline in the Fourier domain, and find the minimum value of the energy function with respect to the components of the surface in the null space of the system matrix. Quantitative and visual comparisons, using simulated data of a fractal and smooth surface, show that the proposed method significantly outperforms the Horn, Zheng-Chellappa, Tsai-Shah and Pentland linear methods for surface reconstruction.