🚀 go-pugleaf

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.