Statistical synthesis of a Bayesian algorithm image segmentation an d measurementof aerial object coordinates

This paper presents the results of a statistical synthesis of an algorithm for segmenting images of aerial objects based on the Bayesian criterion of maximum posterior probability. The key feature of the algorithm is the use of information about the operator’s initial choice of the object to form a priori spatial distribution of coordinates, which allows effectively taking into account geometric constraints on the movement of the object between adjacent frames of the video sequence. A twostage approach has been developed to jointly solve the tasks of pixel classification and object position estimation, in which spatial information is directly integrated into the segmentation decision rule through a Gaussian model of probability distribution. Analytical expressions for the optimal decision rule are obtained in the form of a threshold comparison of the log-likelihood ratio, which includes both intensity and spatial components. The resulting algorithm improves the quality of segmentation and the accuracy of coordinate measurements under varying lighting conditions, which is critically important for automatic tracking systems of aerial objects in the tasks of airspace monitoring and flight trajectory management.