Over the past year, my standout posts have featured camera calibration, Python, C++, OpenCV, NVIDIA optimizations, advanced C++ techniques, AI integration in robotics with LLMs, computer vision, and machine learning. Explore more: Link
In this post, I delved into advanced methods of camera calibration, focusing on geometric analysis and calibration patterns using tools like MATLAB, Python, and OpenCV. I highlighted a C++ algorithm implemented for high-speed, high-accuracy corner detection within calibration patterns, emphasizing rotation and orientation. The process was further refined using subpixel accuracy and noise reduction techniques, enhancing precision in computer vision applications.
Optimizing NVIDIA GPUs for deep learning has been a crucial topic, especially in multi-GPU setups. In this post, I explored how leveraging CUDA and cuDNN can lead to high-performance AI applications such as video analytics, face recognition, and smart systems. By optimizing GPU resources, we can achieve significant performance gains in complex computer vision tasks.
C++ remains a cornerstone in high-performance computing, supporting object-oriented, procedural, and generic programming paradigms. In this post, I explored key features of C++ including:
new/delete and smart pointers.These features make C++ an essential language for developing efficient and robust applications in computer vision and AI.
This comprehensive post explored advanced technologies in robotics, focusing on AI inference, computer vision, and generative AI on robotics platforms. Key highlights included:
Robotics computing processes data locally on devices like smart robots to reduce latency and enhance reliability. This supports real-time decision-making across various robotics applications, offering increased security and reduced network strain.
As we approach the end of 2024, I wanted to reflect on some of my most impactful LinkedIn posts from the year. From deep dives into camera calibration techniques to insights on optimizing VRAM consumption during LLM training, it’s been an exciting journey exploring the forefront of computer vision, robotics, and AI.
In this post, I delved into advanced methods of camera calibration, focusing on geometric analysis and calibration patterns using tools like MATLAB, Python, and OpenCV. I highlighted a C++ algorithm implemented for high-speed, high-accuracy corner detection within calibration patterns, emphasizing rotation and orientation. The process was further refined using subpixel accuracy and noise reduction techniques, enhancing precision in computer vision applications.
Geometric Analysis, Calibration Patterns, MATLAB, Python, C++, OpenCV, Subpixel Precision. A C++ implemented algorithm was used for high-speed, high-accuracy corner detection within calibration patterns, focusing on rotation and orientation. The process was refined by subpixel accuracy and noise reduction techniques.
In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. The correlation between the topographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameters, which constitute the geometric model of camera imaging, are utilized to establish the association among the 3D geometric location of one point and its consistent point in an image. Typically, experiments are conducted to obtain the aforementioned parameters and relevant evaluation, which is a process called camera calibration.
Optimization Methods in Computer Vision
Optimizing NVIDIA GPUs for deep learning has been a crucial topic, especially in multi-GPU setups. In this post, I explored how leveraging CUDA and cuDNN can lead to high-performance AI applications such as video analytics, face recognition, and smart systems.
Deep Learning Optimization Post
Additionally, I discussed monitoring system resources on the NVIDIA Jetson Nano:
sudo /usr/bin/tegrastatssudo -H pip3 install jetson-statssudo jtopMonitoring System Resources Post
On-Device Training in ONNX Runtime (ORT) allows models to be trained directly on edge devices, enhancing user privacy by keeping data local. ORT, a cross-platform engine, supports various machine learning models and now extends to on-device training, making it simpler for developers to train models with data on the device for a personalized experience. This capability is designed to be efficient in memory and performance, fitting the constraints of edge devices and supporting federated learning for privacy-preserving global model updates.