This repository contains the Jupyter Notebook for Final Lab (Part 2) of the Computer Vision 1 course, part of the Master's in Artificial Intelligence program at the University of Amsterdam. The lab exercises are designed to deepen your understanding of key computer vision concepts and provide hands-on experience with popular Python libraries such as OpenCV, NumPy, and Matplotlib.
Digital cameras are everywhere, embedded in consumer cameras, webcams, mobile phones, and professional equipment. These devices generate vast amounts of data, crucial for communication, observation, and interaction. This course focuses on the computational techniques used to enable computers to understand the visual world, with a particular emphasis on scene understanding and object recognition.
The course covers a wide range of topics, including:
- Image formation and filtering
- Feature extraction (color and shape invariants, interest point detectors, descriptors like SIFT and HoG)
- Visual information representation (vector space, statistical models, Bag-of-Words)
- Learning and classification techniques, including deep learning (DL)
- Object detection and classification, object tracking
Students should provide their own Python environment to run the Jupyter Notebook.
cv1-final-lab-part-2/
│
├── .gitignore
├── cv1-final-lab-part-2.ipynb
├── LICENSE
└── README.md