Published February 12, 2024
| Version v1
Journal article
Open
ACTIVE CONTOURS METHOD IMPLEMENTATION FOR OBJECTS SELECTION IN THE MOBILE ROBOT'S WORKSPACE
Creators
- 1. Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
- 2. Faculty of Information Technology, Department of Computer Science, Ajloun National University, Ajloun, Jordan
Description
This article is a study on the implementation of the active contours method using mathematical descriptions to identify objects in the work area of a mobile robot. The program, developed in Python in the PyCharm 2022.2.3 (Professional Edition) environment, is based on the principles of the active contour method, ensuring accurate selection of objects in images. Experiments conducted on matchbox contour extraction with the help of ESP32-Cam module confirm the effectiveness of the method in real-world conditions, demonstrating its potential for application in various fields of mobile robotics and computer vision.
Files
135-145 Vladyslav Y JUSR JURNAL.pdf
Files
(1.2 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:0a466443f23b64e6f9b635e58591a401
|
1.2 MB | Preview Download |
Additional details
References
- 1. Akopov, M., & et al. (2023). Choosing a Camera for 3D Mapping. Journal of Universal Science Research, 1(11), 28-38.
- 2. Yevsieiev, V., & et al. (2022). Software Implementation Concept Development for the Mobile Robot Control System on ESP-32CAM. In Current issues of science, prospects and challenges: collection of scientific papers «SCIENTIA» with Proceedings of the II International Scientific and Theoretical Conference, Sydney, Australia: European Scientific Platform, 2, 54-56.
- 3. Maksymova, S., & Velet, A. (2022). Development of an Automated System of Terminal Access to Production Equipment Using Computer Vision. In Manufacturing & Mechatronic Systems 2022: Proceedings of VIst International Conference, Kharkiv, 22-23.
- 3. Maksymova, S., & Velet, A. (2022). Development of an Automated System of Terminal Access to Production Equipment Using Computer Vision. In Manufacturing & Mechatronic Systems 2022: Proceedings of VIst International Conference, Kharkiv, 22-23.
- 4. Maksymova, S., & Akopov, M. (2023). Selection of Sensors for Building a 3D Model of the Mobile Robot's Environment. In Manufacturing & Mechatronic Systems 2023: Proceedings of VII International Conference, Kharkiv, 33-35.
- 5. Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.
- 6. Deineko, Zh., & et al.. (2021). Color space image as a factor in the choice of its processing technology. Abstracts of I International scientific-practical conference «Problems of modern science and practice» (September 21-24, 2021). Boston, USA, pp. 389-394.
- 7. Putyatin, Y. P., & et al.. (2016) The Pre-Processing of Images Technique for the Material Samples in the Study of Natural Polymer Composites. American Journal of Engineering Research, 5(8), 221-226.
- 8. Lyashenko, V., & et al.. (2021). Wavelet ideology as a universal tool for data processing and analysis: some application examples. International Journal of Academic Information Systems Research (IJAISR), 5(9), 25-30.
- 9. Kobylin, O., & Lyashenko, V. (2014). Comparison of standard image edge detection techniques and of method based on wavelet transform. International Journal, 2(8), 572-580.
- 10. Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.