armin@csri.toronto.edu (Armin Haken) (03/23/88)
AI Seminar: 11AM, Thursday March 31, Sandford Fleming 1101
(Note that the speaker is a candidate for a faculty position.)
BAYESIAN MODELING OF UNCERTAINTY
IN EARLY VISION
Richard Szeliski
Computer Science Deparment
Carnegie Mellon University
Many of the problems in early vision, such as stereo, motion, shape
from shading and surface interpolation, have recently been formalized
using variational calculus, regularization and Markov Random Fields.
These techniques result in well defined computational problems and
lead to algorithms that can incorporate sparse or noisy data from a
variety of sensors. The focus to date has been on finding algorithms
that yield single optimal estimates, without considering the
resulting uncertainty in these estimates. The work in my thesis
starts by interpreting the smoothness constraints used in
regularization as defining a probabilistic prior model. This allows
us to compute the uncertainty in the posterior estimate, and to model
probability distributions over dense fields (such as depth maps) in a
succinct fashion. The uncertainty can be used directly in
applications such as robot navigation. The estimate thus obtained
can also be combined with new measurements to yield an improved
estimate whose uncertainty is reduced over time. This approach,
which is an extension of the Kalman filter to two-dimensional depth
maps, has been used to design an incremental (on-line)
depth-from-motion algorithm. Our results show that this incremental
algorithm performs as well as wide-baseline stereo, and is simpler to
implement.