Analysis of the Possibility of Intellectualization of Algorithms for Estimating the Parameters of Dynamic Systems Based on Adaptive Model of Motion

This article aims to explore the possibility of using the Kalman filter to filter images. The relevance of the study lies in the fact that at present the tasks of image processing have become of great importance in many areas, such as industry, science, medicine, space industry and agriculture. Methods for improving image quality are of great applied and scientific interest for the agricultural sector, since machine vision methods are now widely used in assessing the condition of agricultural plants, soil condition, sorting of agricultural products, controlling unmanned agricultural machines, etc. The purpose of this work is to develop an algorithm and software for filtering grayscale images. The article consists of four parts: Introduction, Materials and methods, Results, Conclusions. The first part describes the relevance of the topic, discusses the reasons for obtaining noisy images. The second part describes the Kalman filter algorithm as applied to image filtering problems. In the third part, the results of the software implementation of the developed algorithm are considered, which make it possible to evaluate the quality of image filtering. In the fourth part conclusions are drawn and summed up. The main results of the work are the algorithmic implementation of noise removal from halftone images grayscale images using a software tool developed as part of these studies.

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Analysis of the Possibility of Intellectualization of Algorithms for Estimating the Parameters of Dynamic Systems Based on Adaptive Model of Motion

Cited by 2 publications

References 12 publications

Synthesis of Estimation System for UAV Orientation with a Neural Network-Identifier

Synthesis of Estimation System for UAV Orientation with a Neural Network-Identifier

Filtering grayscale images using the Kalman filter

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