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Bayesian Order-Consistency Testing with Class Priors Derivation for Robust Change Detection
In this paper we propose a formalization of change
detection as a Bayesian order-consistency test, based on
the assumption that disturbance factors such as illumination
changes and variations of camera parameters do not
change the ordering between noiseless intensities within
a neighborhood of pixels. The assumption of additive,
zero-mean, i.i.d. gaussian noise allows for testing the composite
order-consistency hypothesis by efficient computation
of the marginal likelihood. Moreover, since the above
formalization enables to incorporate changed/unchanged
class priors seamlessly, we also propose a simple method
to derive informative priors based on the calculation of
marginal likelihoods at reduced resolution. Experimental
results on challenging test sequences characterized by sudden
and strong illumination changes prove the effectiveness
of the proposed approach