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In this paper, we present a statistical change detection approach aimed at being robust with respect to the main disturbance factors acting in real-world applications such as illumination changes, camera gain and exposure variations, noise. We rely on modeling the effects of disturbance factors on images as locally order-preserving transformations of pixel intensities plus additive noise. This allows us to identify within the space of all of the possible image change patterns the subspace corresponding to disturbance factors effects. Hence, scene changes can be detected by a-contrario testing the hypothesis that the measured pattern is due to disturbance factors, that is, by computing a distance between the pattern and the subspace. By assuming additive Gaussian noise, the distance can be computed within a maximum likelihood nonparametric isotonic regression framework. In particular, the projection of the pattern onto the subspace is computed by an O(N) iterative procedure known as Pool Adjacent Violators algorithm.