Computer vision is a field of artificial intelligence that enables machines to interpret and understand visual information from the world. This tutorial will guide you through the basics of computer vision, its applications, and key technologies.
🧠 Key Concepts
- Image Processing: Converting raw images into usable data for analysis.
- Feature Detection: Identifying important elements like edges, corners, or textures.
- Object Recognition: Detecting and classifying objects within images.
- Scene Understanding: Interpreting the context and relationships in a visual scene.
🔍 Techniques & Tools
- Convolutional Neural Networks (CNNs): The backbone of modern computer vision models.
- OpenCV: A popular library for real-time image and video processing.
- YOLO (You Only Look Once): Efficient object detection in real-time applications.
- Semantic Segmentation: Assigning labels to each pixel in an image.
🧾 Applications
- Autonomous Vehicles: Detecting pedestrians, traffic signs, and obstacles.
- Medical Imaging: Analyzing X-rays, MRIs, and other diagnostic images.
- Retail: Enhancing customer experience with facial recognition and inventory management.
- Security: Surveillance systems using real-time video analysis.
📚 Further Reading
For a deeper dive into related topics, check out our Deep Learning Tutorial.