Building computer vision systems using machine learning algorithms
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Commission of Motorization and Energetics in Agriculture
Abstract
In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are
offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
Description
Keywords
algorithm, information system, neural network, machine learning, client-server architecture, script, artificial system, machine learning algorithm
Citation
Boyko N. Building computer vision systems using machine learning algorithms / N. Boyko, N. Sokil // Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. – P. 15–20. – Bibliography: 20 titles.