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Sydney, NSW, Australia
Nov. 22, 2006 to Nov. 24, 2006
ISBN: 0-7695-2688-8
pp: 4
Alessandro Lanza , University of Bologna, Italy
Luigi Di Stefano , University of Bologna, Italy
ABSTRACT
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].
INDEX TERMS
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CITATION
Alessandro Lanza, Luigi Di Stefano, "Detecting Changes in Grey Level Sequences by ML Isotonic Regression", AVSS, 2006, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance 2006, pp. 4, doi:10.1109/AVSS.2006.40
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