Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Flows of Matrix-Valued Functions: Application to Diffusion Tensor Regularization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Journal of Computational Physics
Numerical methods for minimization problems constrained to S1 and S2
Journal of Computational Physics
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
An adaptive window mechanism for image smoothing
Computer Vision and Image Understanding
Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
On vector and matrix median computation
Journal of Computational and Applied Mathematics
Hi-index | 0.01 |
We address the problem of restoring, while preserving possible discontinuities, fields of noisy orthonormal vector sets, taking the orthonormal constraints explicitly into account. We develop a variational solution for the general case where each image feature may correspond to multiple n-D orthogonal vectors of unit norms. We first formulate the problem in a new variational framework, where discontinuities and orthonormal constraints are preserved by means of constrained minimization and F-functions regularization, leading to a set of coupled anisotropic diffusion PDE's. A geometric interpretation of the resulting equations, coming from the field of solid mechanics, is proposed for the 3D case. Two interesting restrictions of our framework are also tackled : the regularization of 3D rotation matrices and the Direction diffusion (the parallel with previous works is made). Finally, we present a number of denoising results and applications.