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Bayesian Modeling of Uncertainty in Low-Level Vision (Hardcover, 1989 ed.)
Loot Price: R3,150
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Bayesian Modeling of Uncertainty in Low-Level Vision (Hardcover, 1989 ed.)
Series: The Springer International Series in Engineering and Computer Science, 79
Expected to ship within 10 - 15 working days
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Vision has to deal with uncertainty. The sensors are noisy, the
prior knowledge is uncertain or inaccurate, and the problems of
recovering scene information from images are often ill-posed or
underconstrained. This research monograph, which is based on
Richard Szeliski's Ph.D. dissertation at Carnegie Mellon
University, presents a Bayesian model for representing and
processing uncertainty in low level vision. Recently, probabilistic
models have been proposed and used in vision. Sze liski's method
has a few distinguishing features that make this monograph im
portant and attractive. First, he presents a systematic Bayesian
probabilistic estimation framework in which we can define and
compute the prior model, the sensor model, and the posterior model.
Second, his method represents and computes explicitly not only the
best estimates but also the level of uncertainty of those estimates
using second order statistics, i.e., the variance and covariance.
Third, the algorithms developed are computationally tractable for
dense fields, such as depth maps constructed from stereo or range
finder data, rather than just sparse data sets. Finally, Szeliski
demonstrates successful applications of the method to several real
world problems, including the generation of fractal surfaces,
motion estimation without correspondence using sparse range data,
and incremental depth from motion."
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