- Tytuł:
- Building computer vision systems using machine learning algorithms
- Autorzy:
-
Boyko, N.
Sokil, N. - Powiązania:
- https://bibliotekanauki.pl/articles/410768.pdf
- Data publikacji:
- 2017
- Wydawca:
- Polska Akademia Nauk. Oddział w Lublinie PAN
- Tematy:
-
algorithm
information system
neural network
machine learning
client-server architecture
script
artificial system
machine learning algorithms
algorytm
systemy informacyjne
sieci neuronowe
systemy uczące
architektura klient-serwer
skrypt - Opis:
- 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.
- Źródło:
-
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 15-20
2084-5715 - Pojawia się w:
- ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
- Dostawca treści:
- Biblioteka Nauki
Artykuł