Certainty color maps compared to histograms

We present a new segmentation algorithm based on probabilistic histograms and introduce certainty calculus and certainty color maps to solve the difficult problem of histogram separation. This new method is then compared to simple histogram and normalized histogram techniques. Using a set of experiments designed to measure the quality of segmentation, we have shown that certainty color maps is a much more accurate approach than histograms, especially when histogram separation in necessary.