Threshold selection based on histogram modeling
This research is concerned with modeling the gray-level probability density function of the image and then using it to select optimal threshold values. Image histogram modeling by kernel density estimation is considered. The main goal is to model the histogram properly. A number of analytic techniques for threshold selection are discussed and empirically evaluated on test as well as real-world scenes.<<ETX>>