1 research outputs found
Detecting Changes in Grey Level Sequences by ML Isotonic Regression
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]