Econtechmod. – 2017. – Vol. 6, No. 2

Permanent URI for this collectionhttps://ena.lpnu.ua/handle/ntb/39409

Науковий журнал

International quarterly journal on economics in technology, new technologies and modelling processes.

Econtechmod : an international quarterly journal on economics in technology, new technologies and modelling processes. – Lublin ; Rzeszow, 2017. – Volum 6, number 2. –96 p.

Browse

Filters

20172

Settings

Has files: YesSubject: search.filters.subject.machine learning

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Analysis of computer vision and image analysis technics
    (Commission of Motorization and Energetics in Agriculture, 2017) Rybchak, Z.; Basystiuk, O.; Lviv Polytechnic National University
    Computer vision and image recognition are one of the most popular theme nowadays. Moreover, this technology developing really fast, so filed of usage increased. The main aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in computer vision and image recognition.
  • Thumbnail Image
    Item
    Building computer vision systems using machine learning algorithms
    (Commission of Motorization and Energetics in Agriculture, 2017) Boyko, N.; Sokil, N.; Lviv Polytechnic National University
    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.