Markov networks for low-level vision
Bill Freeman and Egon Pasztor
We seek a learning-based algorithm that applies to various low-level
vision problems. For each problem, we want to find the scene
interpretation that best explains image data. For example, we may
want to infer the projected velocities (scene) which best explain two
consecutive image frames (image).
>From synthetic data, we model the statistical relationship between
local image and scene regions, and between neighboring scene regions.
Given a new image, we then propagate Bayesian beliefs in the Markov
network to infer the underlying scene. (In joint work with Y. Weiss,
we justify using belief propagation in loopy networks.) This yields
an efficient method to infer low-level scene interpretations, which we
call VISTA, Vision by Image-Scene Training.
We apply this to the "super-resolution" problem (estimating high
frequency details from a low-resolution image), showing good results.
For the motion estimation problem, we show resolution of the aperture
problem and filling-in arising from application of the same
probabilistic machinery.