Name
Abstract | ||
|---|---|---|
This paper investigates structure tensor field regularization applied to directional textured image analysis. From previous works on tensor filtering, we demonstrate that, knowing that the structure tensor is a specific tool coding the local geometry of the image, the tensor field filtering process must be driven by a geometric dissimilarity measure to define the adaptability of the smoothing process. We propose a new dissimilarity measure combining two terms devoted respectively to the orientation and to the shape component of the tensor. This intelligible encoding exhibiting the geometric structure of the image enables us to overcome major drawbacks of conventional Euclidean and Riemannian approaches for which the dissimilarity measure emphasizes only the local manifold geometry. Finally, for seismic imaging application, our method compared to existing ones shows that relevant information can be extracted by enhancing the seismic structures identification. |
Year | Venue | Keywords |
|---|---|---|
2010 | Aalborg | geometry,image texture,smoothing methods,tensors,euclidean approaches,riemannian approaches,directional textured image analysis,geometric dissimilarity measure,intelligible encoding,local manifold geometry,orientation,seismic imaging application,seismic structures identification,shape component,smoothing process,structure tensor field regularization,tensor field filtering process,diffusion tensor imaging,shape,filtering,tensile stress |
Field | DocType | ISSN |
Computer vision,Tensor,Tensor field,Cartesian tensor,Algorithm,Stress (mechanics),Smoothing,Regularization (mathematics),Structure tensor,Artificial intelligence,Euclidean geometry,Mathematics | Conference | 2219-5491 |
Citations | PageRank | References |
5 | 0.45 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent Toujas | 1 | 5 | 0.45 |
| Marc Donias | 2 | 45 | 7.92 |
| Y. Berthoumieu | 3 | 389 | 51.66 |