International Journal of Computer Techniques Volume 12 Issue 3 | Revolutionizing Machine Perception and Intelligence Through OpenCV Technology
Revolutionizing Machine Perception and Intelligence Through OpenCV Technology
International Journal of Computer Techniques – Volume 12 Issue 3, May – June – 2025
Abstract
Humans perceive the world through vision, and image processing strives to empower machines with similar capabilities. OpenCV, a robust library of programming functions, acts as the brain behind computer vision tasks, enabling machines to interpret and analyze visual data.
Despite its widespread adoption, OpenCV faces challenges in handling large datasets effectively, limiting its scalability for modern applications. This paper delves into these challenges and introduces a novel approach to optimize OpenCV for large dataset processing, thereby addressing its current limitations.
Keywords
OpenCV, large datasets, optimization, computer vision, scalability.
Conclusion
The primary interface of OpenCV is written in C++, with full interfaces available in Python, Java, and MATLAB. Since 2010, a CUDA-based GPU interface has been in development.
OpenCV can run on various platforms, including Windows, Android, iOS, and Linux. While OpenCV currently does not perform well on big data processing, the improvements and techniques mentioned earlier can enhance its performance and make it more effective for handling such tasks.
References
- Sharma, D., & Bhatia, R. (2024). Efficient Image Processing on Edge Devices Using Lightweight OpenCV Pipelines. Journal of Embedded Systems and Vision.
- Patel, V., & Mehta, S. (2024). GPU-Accelerated OpenCV Framework for Real-Time Traffic Surveillance. International Journal of Intelligent Systems.
- Kaur, N., & Reddy, M. (2024). A Comparative Study of OpenCV and Deep Learning Frameworks in Medical Imaging. Medical Image Analysis and AI.
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