Detecting Changes in Grey Level Sequences by ML Isotonic Regression

We present a robust and efficient change detection algorithm for grey-level sequences. A deep investigation of the effects of disturbance factors (illumination changes and automatic or manual adjustments of the camera transfer function, such as AGC, AE and gamma-correction) on image brightness allows to assume locally an order-preservation of pixel intensities. By a simple statistical modelling of camera noise, an ML isotonic regression procedure can thus be applied to perform change detection. Although the proposed approach may be used as a stand-alone pixel-level change detector, here we apply it to reduced-resolution images. In fact, we aim at using the algorithm as the coarse-level of a coarse-to-fine change detector we presented in [2].