Bayesian Object Localisation in Images
Josephine Sullivan, Andrew Blake, Michael Isard and John MacCormick
International Journal of Computer Vision 44 (2): 111-135, September 2001
Abstract
A Bayesian approach to intensity-based object localisation is
presented that employs a learned 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 in the foreground (i.e. over
the object), and in the background, to be modelled. Some previous
approaches to object localisation by Monte Carlo methods have used
models which, we claim, do not fully address the issue of the
statistical independence of image intensities. It is addressed here by
applying to each image a bank of filters whose outputs are
approximately statistically independent. Distributions of the
responses of individual filters, over foreground and background, are
learned from training data. These distributions are then used to
define a joint distribution for the output of the filter bank,
conditioned on object configuration, and this serves as an observation
likelihood for use in probabilistic inference about localisation. The
effectiveness of probabilistic object localisation in image clutter,
using Bayesian Localisation, is illustrated. Because it is a Monte
Carlo method, it produces not simply a single estimate of object
configuration, but an entire sample from the posterior distribution
for the configuration. This makes sequential inference of
configuration possible. Two examples are illustrated here: coarse to
fine scale inference, and propagation of configuration estimates over
time, in image sequences.
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