Illumination-Invariant Change Detection Using a Statistical Colinearity Criterion

This paper describes a new algorithm for illumination-invariant change detection that combines a simple multiplicative illumination model with decision theoretic approaches to change detection. The core of our algorithm is a new statistical test for linear dependence (colinearity) of vectors observed in noise. This criterion can be employed for a significance test, but a considerable improvement of reliability for real-world image sequences is achieved if it is integrated into a Bayesian framework that exploits spatio-temporal contiguity and prior knowledge about shape and size of typical change detection masks. In the latter approach, an MRF-based prior model for the sought change masks can be applied successfully. With this approach, spurious spot-like decision errors can be almost fully eliminated.