| S.H. Lai and B.C. Vemuri. Physically-Based Adaptive Preconditioning for Early Vision, PAMI(19), June 1997. |
....shape from shading (SFS) within the deformable models framework. Most of the earlier work on SFS has been compiled in [7] the first comprehensive comparative study of a number of SFS algorithms is [21] Most of the methods use a regularization approach combined with some additional constraints [3, 8, 11, 9, 22, 12]. 9, 4] combine stereo and shading; 4] handles perspective projection in their stereo and shading mesh. Other approaches are described in [6, 16, 10, 20] A useful discussion of the ambiguities involved in light source estimation can be found in [2] A number of researchers have proposed ....
S.H. Lai and B.C. Vemuri. Physically-Based Adaptive Preconditioning for Early Vision, PAMI(19), June 1997.
....we can use Cholesky decomposition for the tridiagonal SPD matrix (A 0 p j I) to compute the update solution. This only takes O(N) time per iteration. The parameters p j are called ADI parameters and J is the number of iterations needed. They are chosen according to the method described in [25]. 4 Model Fitting Results In this section, we present a set of ve experiments, two of these experiments are in the medical imaging domain and demonstrate model tting to sparse 3D data points placed by an expert neuroscientist along the boundaries of a hippocampus and a gyrus in selected ....
S. H. Lai and B. C. Vemuri, \Physically-based adaptive preconditioning for early vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 594-607, 1997.
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