We present four new change detection methods that create an automated change map from a probability map. In this
case, the probability map was derived from a 3D model. The primary application of interest is aerial photographic
applications, where the appearance, disappearance or change in position of small objects of a selectable class (e.g., cars)
must be detected at a high success rate in spite of variations in magnification, lighting and background across the image.
The methods rely on an earlier derivation of a probability map. We describe the theory of the four methods, namely
Bernoulli variables, Markov Random Fields, connected change, and relaxation-based segmentation, evaluate and
compare their performance experimentally on a set probability maps derived from aerial photographs.
Over the last several years, a new representation for geometry has been developed, based on a 3-d probability
distribution of surface position and appearance. This representation can be constructed from multiple images, using both
still and video data. The probability for 3-d surface position is estimated in an on-line algorithm using Bayesian
inference. The probability of a point belonging to a surface is updated as to its success in accounting for the intensity of
the current image at the projected image location of the point. A Gaussian mixture is used to model image appearance.
This update process can be proved to converge under relatively general conditions that are consistent with aerial
imagery. There are no explicit surfaces extracted, but only discrete surface probabilities. This paper describes the
application of this representation to object recognition, based on Bayesian compositional hierarchies.