A Bayesian approach to object detection

A binary hypothesis testing is a common tool in the task of object detection. Experiments with real images have confirmed an effectiveness of Neyman-Pearson Detectors based on locally weighted sample mean and variance. In line with Bayesian approach threshold parameter will be defined as a function of prior distribution. According to the basic idea of Gibbs Sampler the neighbourhood system of image element determines distribution of this element. Using concepts of the first part of this paper and taking any image as an initial lye can form sequence of images in order to develop this dependence. As a result quality of object detection will be improved significantly.