A Bayesian approach to intensity-based object localisation is presented tha
t employs a teamed probabilistic model of image filter-bank output, applied
via Monte Carlo methods, to escape the inefficiency of exhaustive search.
An adequate probabilistic account of image data requires intensities both i
n the foreground (i.e, over the object), and in the background, to be model
led. Some previous approaches to object localisation by Monte Carlo methods
have used models which, we claim, do not fully address the issue of the st
atistical independence of image intensities. It is addressed here by applyi
ng to each image a bank of filters whose outputs are approximately statisti
cally independent. Distributions of the responses of individual filters, ov
er foreground and background, are learned from training data. These distrib
utions are then used to define a joint distribution for the output of the f
ilter bank, conditioned on object configuration, and this serves as an obse
rvation likelihood for use in probabilistic inference about localisation.
The effectiveness of probabilistic object localisation in image clutter, us
ing Bayesian Localisation, is illustrated. Because it is a Monte Carlo meth
od, it produces not simply a single estimate of object configuration, but a
n entire sample from the posterior distribution for the configuration. This
makes sequential inference of configuration possible. Two examples are ill
ustrated here: coarse to fine scale inference, and propagation of configura
tion estimates over time, in image sequences.