Abstract
Minimal surface regularization has been used in several applications ranging from stereo to image segmentation, sometimes hidden as a graph-cut discrete formulation, or as a strictly convex approximation to TV minimization. In this paper we consider a modified version of minimal surface regularization coupled with a robust data fitting term for interpolation purposes, where the corresponding evolution equation is constrained to diffuse only along the isophotes of a given image u and we design a convergent numerical scheme to accomplish this. To illustrate the usefulness of our approach, we apply this framework to the digital elevation model interpolation and to constrained vector probability diffusion.
| Original language | English |
|---|---|
| Pages | 1049-1058 |
| Number of pages | 10 |
| State | Published - 2006 |
| Event | 2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh, United Kingdom Duration: 4 Sep 2006 → 7 Sep 2006 |
Conference
| Conference | 2006 17th British Machine Vision Conference, BMVC 2006 |
|---|---|
| Country/Territory | United Kingdom |
| City | Edinburgh |
| Period | 4/09/06 → 7/09/06 |