The history of computer vision: Discover how machine vision evolved from simple algorithms to complex AI systems that create art and interpret our world today.
- Marvin Minsky and Seymour Papert lay the foundations
- ‘Perceptrons’ and their limits
- The challenges of early pattern recognition
- Canny edge detector revolutionises image recognition
- SIFT enables robust feature recognition
- First steps towards automated image interpretation
- Viola Jones algorithm: real-time facial recognition becomes reality
- Support Vector Machines optimise image classification
- AI begins to understand visual patterns
- AlexNet heralds a new era
- ResNet enables deeper networks and better accuracy
- YOLO brings object recognition in real time
- Self-supervised learning with SimCLR
- Vision Transformers: NLP techniques conquer image processing
- CLIP and DALL-E: AI understands and creates visual art
- The call for interpretable models
- Robustness against adversarial attacks
- Efficiency for mobile devices and real-time applications
- Ethical issues of AI-supported surveillance
The journey of computer vision from simple edge detectors to multifunctional visual systems is breathtaking. Today’s AI systems are approaching or even surpassing human performance in many areas. The future promises even more fascinating developments.