Computer Vision

Course description

This course covers fundamental topics in computer vision. The course will provide an introduction to image formation, image processing, feature detection, segmentation, multiple view geometry and 3D reconstruction, motion, object recognition and classification.

Learning outcome

Upon successful completion of this class, you will be able to:

1. Understand how cameras capture images of a scene Implement
2. Implement and use: algorithms for image processing such as image filtering and image segmentation;
   algorithms for object detection (such as faces) and recognition;
   algorithms for 3D reconstruction (e.g., from stereo systems) 
3. Describe the mathematics underpinning each method and know how to adapt it to new scenarios.

Prerequisites

The course requires students to be familiar with the basics of linear algebra, probability theory and Python programming. A brief review of these subjects will be carried out during the exercise sessions.

Resources

The following books are recommended as additional reading:

• Computer Vision : A Modern Approach, David A. Forsyth and Jean Ponce.
• Pattern Recognition and Machine Learning, Christopher Bishop.
• Algorithms and Applications, Rick Szeliski.
  An electronic copy is available for free online. 
• Visual Object Recognition, Kristen Grauman and Bastian Leibe.
  This book is also available online for free.
• Computer Vision: Models, Learning, and Inference, Simon J.D. Prince.

Assessment

There will be 2 assignments and the deadlines will be specified at the beginning of the course.
For admission to the examination one must pass every assignment. The assignments will also
contribute to 30% of the final mark. The description of each assignment will be made available
in Ilias. Notice that assignments will require use of Python.
All the homework assignments are also intended for exam preparation.