LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE

Abstract

The article was aimed at studying the process of learning by the local difference threshold when filtering normal white noise. The existing learning algorithms for image processing were analyzed and their advantages and disadvantages were identified. The influence of normal white noise on the recognition process is considered. A method for organizing the learning process of the correlator with image preprocessing by the GQP method has been developed. The dependence of the average value of readings of the rank CCF (RCCF) of GQPs of the reference and current images, representing realizations of normal white noise, on the probability of formation of readings of zero GQP is determined. Two versions of the learning algorithm according to the described learning method are proposed. A technique for determining the algorithm efficiency estimate is proposed.