Improving the Efficiency of the Method of Stochastic Gradient Identification of Objects in Binary and Grayscale Images Using Their Preprocessing

Abstract

In this paper, we consider ways to improve the stochastic gradient method efficiency of object identification for binary and grayscale images using methods of image preprocessing. Identification of an object is understood as the recognition of an object on the image with its parameters estimation. Low-pass filtering and image equalization are considered as preliminary processing. The identification parameters convergence rate is investigated. The optimal sizes of Gaussian filter mask for binary and grayscale images were found based on COIL-20 images.