Name
Abstract | ||
|---|---|---|
In this paper we show that a classic optical flow technique by Nagel and Enkelmann (1986, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, pp. 565–593) can be regarded as an early anisotropic diffusion method with a diffusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and the smoothness term in different images, (ii) use a linear scale-space focusing strategy from coarse to fine scales for avoiding convergence to physically irrelevant local minima, and (iii) create an energy functional that is invariant under linear brightness changes. Applying a gradient descent method to the resulting energy functional leads to a system of diffusion–reaction equations. We prove that this system has a unique solution under realistic assumptions on the initial data, and we present an efficient linear implicit numerical scheme in detail. Our method creates flow fields with 100 % density over the entire image domain, it is robust under a large range of parameter variations, and it can recover displacement fields that are far beyond the typical one-pixel limits which are characteristic for many differential methods for determining optical flow. We show that it performs better than the optical flow methods with 100 % density that are evaluated by Barron et al. (1994, Int. J. Comput. Vision, Vol. 12, pp. 43–47). Our software is available from the Internet. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1023/A:1008170101536 | International Journal of Computer Vision |
Keywords | Field | DocType |
image sequences,optical flow,differential methods,anisotropic diffusion,linear scale-space,regularization,finite difference methods,performance evaluation | Anisotropic diffusion,Computer vision,Gradient descent,Maxima and minima,Regularization (mathematics),Artificial intelligence,Finite difference method,Invariant (mathematics),Energy functional,Optical flow,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
39 | 1 | 1573-1405 |
Citations | PageRank | References |
143 | 8.07 | 34 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Alvarez | 1 | 285 | 39.37 |
| Joachim Weickert | 2 | 5489 | 391.03 |
| Javier Sánchez | 3 | 383 | 31.84 |