We present a robust and ecient change detection algorithm for grey-level sequences. A deep investigation of the eects of disturbance factors (illumination changes and automatic or manual adjustments of the camera transfer function, such as AGC, AE and -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-ne change detector we presented in [2].
Detecting Changes in Grey Level Sequences by ML Isotonic Regression / A. Lanza; L. Di Stefano. - ELETTRONICO. - (2006). (Intervento presentato al convegno IEEE Conference on Advanced Video and Signal Based Surveillance tenutosi a Sidney nel 22-24 Novembre 2006).
Detecting Changes in Grey Level Sequences by ML Isotonic Regression
LANZA, ALESSANDRO;DI STEFANO, LUIGI
2006
Abstract
We present a robust and ecient change detection algorithm for grey-level sequences. A deep investigation of the eects of disturbance factors (illumination changes and automatic or manual adjustments of the camera transfer function, such as AGC, AE and -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-ne change detector we presented in [2].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.