Thresholding for Change Detection

Image differencing is used for many applications involving change detection. Although it is usually followed by a thresholding operation to isolate regions of change there are few methods available in the literature specific to (and appropriate for) change detection. We describe four different methods for selecting thresholds that work on very different principles. Either the noise or the signal is modelled, and the model covers either the spatial or intensity distribution characteristics. The methods are: 1/ a Normal model is used for the noise intensity distribution, 2/ signal intensities are tested by making local intensity distribution comparisons in the two image frames (i.e. the difference map is not used), 3/ the spatial properties of the noise are modelled by a Poisson distribution, and 4/ the spatial properties of the signal are modelled as a stable number of regions (or stable Euler number).

• Type: Conference
• Year: 1998
• Where: ICCV
• Authors: Paul L. Rosin
• Keywords: Computer Vision, ICCV 1998, Intensity Distribution Characteristics, Intensity Distribution Comparisons, Noise Intensity Distribution, Spatial Properties, Stable Euler Number