Bayesian Multi-scale Differential Optical Flow
In:
Handbook of Computer Vision and Applications
eds. B Jähne, H Haussecker, and P Geissler,
Volume II, chapter 14, pages 397-422.
Academic Press, April 1999.
The estimation of optical flow is generally assumed to be the first
goal of motion processing in machine vision systems, and is also crucial
for efficient representation of image sequences (e.g., MPEG). The
most common methods of computing optical flow are correlation,
gradient, spatiotemporal filtering, and Fourier phase or energy
techniques. Correlation (usually over a local window) is by far the
most prevalent technique, presumably due to a combination of
intuitive directness and ease of hardware implementation. But
gradient implementations are often more accurate. In addition, the
gradient solution is efficient (because the solution can be computed
analytically rather than via optimization), and produces sub-pixel
displacement estimates. A drawback of the gradient approach is that
it may only be used for small displacements, but this difficulty can
be alleviated using a multi-scale coarse-to-fine algorithm. This
chapter provides a practical description of a Bayesian multi-scale
gradient-based optical flow estimation algorithm, based on work
previously published
[simoncelli-cvpr91,
simoncelli-phd,
Simoncelli93d].
Download
Full text (ps.gz, 407k)
/
Full text (pdf, 510k)
/
EPS Online Publications