RECOGNITION QUALITY OF SIMPLE OBJECTS IN A NEURAL NETWORK
The problem of determining the probability of confusion when recognizing images of simple objects on the background of Gaussian noise in a neural network is considered. It is shown that the probability of confusion between objects is proportional to the number of image pixels in which the images of two objects do not coincide.