Criterion for Automatic Selection of the Most Suitable Maximum-Likelihood Thresholding Algorithm for Extracting Object from their Background in a Still Image

Three Maximum-Likelihood thresholding algorithms based on population mixture models are investigated and a criterion is introduced for automatic selection of the most suitable Maximum-Likelihood thresholding algorithm for extracting object from their background in an arbitrary still image. The most suitable algorithm is that whose population mixture model approximates better the probability density function of the intensity values. The probability density function is estimated from the histogram of the intensity values. The criterion was implemented and applied to real images with different illumination conditions. A subjective analysis of the experimental results showed that for each image,the proposed criterion was always able to select automatically from the three algorithms the one which delivers the best thresholding results.