A General Motion Model and Spatio-Temporal Filters forComputing Optical Flow
International Journal of Computer Vision(1997)
摘要
Traditional optical flow algorithms assume local image translational
motion and apply simple
image filtering techniques. Recent studies have taken two separate
approaches toward
improving the accuracy of computed flow: the application of
spatio-temporal filtering
schemes and the use of advanced motion models such as the affine
model. Each has achieved
some improvement over traditional algorithms in specialized
situations but the computation
of accurate optical flow for general motion has been elusive. In this
paper, we exploit the
interdependency between these two approaches and propose a unified
approach. The general
motion model we adopt characterizes arbitrary 3-D steady motion.
Under perspective
projection, we derive an image motion equation that describes the
spatio-temporal relation
of gray-scale intensity in an image sequence, thus making the
utilization of 3-D filtering
possible. However, to accommodate this motion model, we need to
extend the filter design to
derive additional motion constraint equations. Using Hermite
polynomials, we design
differentiation filters, whose orthogonality and Gaussian derivative
properties insure
numerical stability; a recursive relation facilitates application of
the general nonlinear
motion model while separability promotes efficiency. The resulting
algorithm produces
accurate optical flow and other useful motion parameters. It is
evaluated quantitatively
using the scheme established by Barron et al. (1994) and
qualitatively with real images.
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关键词
Hermite polynomial,evaluation,motion estimation
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