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)
Otsu’s method of image segmentation selects an optimum threshold by maximizing the between-class variance in a gray image. However, this method becomes very time-consuming when extended to a multi-level threshold problem due to the fact that a large
This paper proposes a new method of image thresholding by using cluster organization from the histogram of an image. A new similarity measure proposed is based on inter-class variance of the clusters to be merged and the intra-class variance of the
