In this paper, we present two new, very efficient and accurate algorithms for computing optical flow. The first is a modified gradient-based regularization method, and the other is an SSD-based regularization method. To amend the errors in the image flow constraint caused by the discontinuities in the brightness function, we propose to selectively combine the image flow constraint and the contour-based flow constraint into the data constraint in a regularization framework. The image flow constraint is disabled in the neighborhood of discontinuities, while the contour-based flow constraint is active at discontinuity locations. To solve the linear system resulting from the regularization formulation, the incomplete Cholesky preconditioned conjugate gradient algorithm is employed, leading to an efficient algorithm. Our SSD-based regularization method uses the SSD measure as the data constraint in a regularization framework. The preconditioned nonlinear conjugate gradient with a modified search direction scheme is developed to minimize the resulting energy function. Experimental results for these two algorithms are given to demonstrate their performance.
Robust and efficient algorithms for optical flow computation
01.01.1995
1357882 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Robust and Efficient Algorithms for Optical Flow Computation
British Library Conference Proceedings | 1995
|British Library Online Contents | 1998
|Reliable and Efficient Computation of Optical Flow
British Library Online Contents | 1998
|Robust Optical Flow Computation Based on Least-Median-of-Squares Regression
British Library Online Contents | 1999
|Robust Computation of Optical Flow in a Multi-Scale Differential Framework
British Library Online Contents | 1995
|