The Optical Flow Method and Graham’s Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot’s Workspace
Vol. 2 No. 3 (2024), Articles
Vol. 2 No. 3 (2024)

The Optical Flow Method and Graham’s Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot’s Workspace

Articles
Published 2024-03-07
Amer Abu-Jassar
Faculty of Information Technology, Department of Computer Science, Ajloun National University, Ajloun, Jordan
Vladyslav Yevsieiev
Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Svitlana Maksymova
Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
PDF
DOI

Keywords

Industry 5.0, Computer Vision Systems, Mobile Robots

How to Cite

Amer Abu-Jassar, Vladyslav Yevsieiev, & Svitlana Maksymova. (2024). The Optical Flow Method and Graham’s Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot’s Workspace. Journal of Universal Science Research, 2(3), 64–75. Retrieved from https://universalpublishings.com/index.php/jusr/article/view/4695
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

This article examines the optical flow method and graham algorithm implementation features for searching for the object contour in the mobile robot’s workspace. The mathematical models of both methods were discussed in detail and then implemented in a Python program using the PyCharm development environment. As part of the study, a number of experiments were carried out, the purpose of which was to evaluate the performance of the optical flow method and the Graham algorithm for extracting the contour of an object. The research results presented in the article highlight the effectiveness of the optical flow method and the Graham algorithm in real-time conditions.

PDF
DOI

References

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.

Matarneh, R., Maksymova, S., Deineko, Z., & Lyashenko, V. (2017). Building robot voice control training methodology using artificial neural net. International Journal of Civil Engineering and Technology, 8(10), 523-532.

Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

Sotnik, S., & Lyashenko, V. (2022). Prospects for Introduction of Robotics in Service. Prospects, 6(5), 4-9.

Ahmad, M. A., Sinelnikova, T., Lyashenko, V., & Mustafa, S. K. (2020). Features of the construction and control of the navigation system of a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(4), 1445-1449.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

Lyashenko, V., & Sotnik, S. (2020). Analysis of Basic Principles for Sensor System Design Process Mobile Robots. Journal La Multiapp, 1(4), 1-6.

Matarneh, R., & et al.. (2019). Development of an Information Model for Industrial Robots Actuators. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), 16(1), 61-67.

Yevsieiev, V., & et al. (2024). Active Contours Method Implementation for Objects Selection in the Mobile Robot’s Workspace. Journal of Universal Science Research, 2(2), 135-145.

Akopov, M., & et al. (2023). Choosing a Camera for 3D Mapping. Journal of Universal Science Research, 1(11), 28-38.

Yevsieiev, V., Abu-Jassar, A., & Maksymova, S. (2024). Object Recognition And Tracking Method in the Mobile Robot’s Workspace in Real Time. Technical Science Research In Uzbekistan,2(2), 115-124.

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, P. 22-23.

Yevsieiev, V., & et al. (2024). Using Contouring Algorithms to Select Objects in the Robots’ Workspace. Technical Science Research In Uzbekistan, 2(2), 32-42.

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.

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.

Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

Matarneh, R., Tvoroshenko, I., & Lyashenko, V. (2019). Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space. International Journal of Recent Technology and Engineering, 8(4), 1687-1693.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.

Boboyorov Sardor Uchqun o‘g‘li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Image Processing Techniques as a Tool for the Analysis of Liver Diseases. Journal of Universal Science Research, 1(8), 223–233.

Abu-Jassar, A., & et al. (2023). Obstacle Avoidance Sensors: A Brief Overview. Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

Borysov, H., & et al. (2023). Parameters for Mobile Robot Kinematic Model Development Determination. Multidisciplinary Journal of Science and Technology, 3(4), 85-91.

Basiuk, V., & et al. (2023). Mobile Robot Position Determining Using Odometry Method. Multidisciplinary Journal of Science and Technology, 3(3), 227-234.

Igor, N., & et al. (2023). Using Mecanum Wheels for Radio Shuttle. Multidisciplinary Journal of Science and Technology, 3(3), 182-187.

Dadkhah, M., Lyashenko, V. V., Deineko, Z. V., Shamshirband, S., & Jazi, M. D. (2019). Methodology of wavelet analysis in research of dynamics of phishing attacks. International Journal of Advanced Intelligence Paradigms, 12(3-4), 220-238.

Sotnik, S., Matarneh, R., & Lyashenko, V. (2017). System model tooling for injection molding. International Journal of Mechanical Engineering and Technology, 8(9), 378-390.

Babker, A., & Lyashenko, V. (2018). Identification of megaloblastic anemia cells through the use of image processing techniques. Int J Clin Biomed Res, 4, 1-5.

Khan, A., Ahmad, M., Joshi, S., & Lyashenko, V. (2016). Synthesis of alumina fibre by annealing method using coir fibre. American Chemical Science Journal, 15(2), 1-7.

An, P. T., & et al. (2021). A modified Graham’s convex hull algorithm for finding the connected orthogonal convex hull of a finite planar point set. Applied Mathematics and Computation, 397, 125889.

Alshamrani, R., & et al. (2020). A preprocessing technique for fast convex hull computation. Procedia Computer Science, 170, 317-324.

Aung, N. L., & et al. (2020). Developed algorithm for making up convex hull based on binary tree. In 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), IEEE, 1-6.

Ferrada, H., & et al. (2020). A filtering technique for fast Convex Hull construction in R2. Journal of Computational and Applied Mathematics, 364, 112298.

Nemirko, A. P., & Dulá, J. H. (2021). Machine learning algorithm based on convex hull analysis. Procedia Computer Science, 186, 381-386.

Xue, H., & et al. (2021). Cross Product and Partitioned Filtering-Based Graham Convex Hull for Buoy Drifting Area Demarcating. Scientific Programming, 2021, 1-9.

Ma, K., & et al. (2020). A strip steel wave-edge detection algorithm based on convex hull detection. In Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), SPIE, 11373, 167-172.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.