2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (2006)
Sydney, NSW, Australia
Nov. 22, 2006 to Nov. 24, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.40
Alessandro Lanza , University of Bologna, Italy
Luigi Di Stefano , University of Bologna, Italy
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 .
Alessandro Lanza, Luigi Di Stefano, "Detecting Changes in Grey Level Sequences by ML Isotonic Regression", 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, vol. 00, no. , pp. 4, 2006, doi:10.1109/AVSS.2006.40