MANAGEMENT OF ARTIFICIAL NEURAL NETWORKS FOR RECOGNITION MAPPING OF HIGH-DEFINITION IMAGES

Abstract

Background. Scientific article reveals the problem of analyzing, recognizing and managing highdefinition images with a minimum error due to the previous frame-by-frame recognition of a complex of lowdefinition images. The fundamental problem is the appearance and impact of gradient noise in the form of disaggregated pixel segments, which significantly reduce the resolution of the area under consideration. Materials and methods. Until now, this area of research on artificial neural networks has not been sufficiently studied due to low consumer demand for the technology and slow development from enthusiasts. Despite the fact that image recognition was not a promising direction before, at the moment it holds potential in the field of application of artificial neural networks and gradient noise leveling with deep learning based on them. Results and conclusions. The article considers both the possibility of adapting old existing approaches to solving the problem of pattern analysis and recognition, and a new control method based on a complex of storyboarding artificial neural networks with further integration for deep learning and solving problems.

Keywords

• image
• artificial neural network
• deep learning
• gradient noise
• mapping