Computing optical flow
- 7 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The authors examine some computational aspects of determining optical flow. Both area- and curve-based approaches are discussed. Necessary and sufficient conditions are investigated for the existence and uniqueness of the smoothing spline from regularization schema prevalent. The authors discuss a variety of boundary constraints: free, Neuman, Dirichlet, and two-point boundary conditions. It is shown that both free and Neuman boundary problems are ill-conditioned, and are not appropriate for optical flow computation. This partly explains why practitioners have attested to the difficult of computing flow velocities using such regularization scheme. Therefore, it is necessary to use either Dirichlet boundary conditions or design different regularization schema. As a common practice in early vision, a continuous problem is formulated, and a discrete version of the problem is solved instead. The authors estimate the discretization errors, and compute the resulting discrete smoothing splines. They study efficient algorithms for solving the system of linear equations for the discrete smoothing splines. Among a variety of iterative algorithms, they propose the Chebyshev method for the computation of the area-based optical flow Author(s) Lee, D. AT&T Bell Labs., Murray Hill, NJ, USA Papageorgiou, A. ; Wasilkowski, G.W.Keywords
This publication has 9 references indexed in Scilit:
- Direct analytical methods for solving Poisson equations in computer vision problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Ill-posed problems in early visionProceedings of the IEEE, 1988
- Dynamic Stereo: Passive Ranging to Moving Objects from Relative Image FlowsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1986
- On the Optimal Solution of Large Linear SystemsJournal of the ACM, 1984
- Monocular depth perception from optical flow by space time signal processingProceedings of the Royal Society of London. B. Biological Sciences, 1983
- Displacement vectors derived from second-order intensity variations in image sequencesComputer Vision, Graphics, and Image Processing, 1983
- Determining optical flowArtificial Intelligence, 1981
- Velocity determination in scenes containing several moving objectsComputer Graphics and Image Processing, 1979
- Depth measurement by motion stereoComputer Graphics and Image Processing, 1976