Computer Vision Methods

The course covers core computer vision problems: search for correspondences between images, 3D reconstruction, object detection, recognition, segmentation of objects in images and videos, image retrieval from large databases and tracking of objects in video sequences. In the labs, the students implement selected methods and test performance on real-world problems.

This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.

The methods for image registration, retrieval and for object detection and tracking are explained. In the labs, the students implement selected methods and test performance on real-world problems.

The course uses grades A-F. Work during the semester 50%, written part of the exam 40%, oral part of the exam 10% (both parts of the exam are online for EuroTeQ students). For details, see the course website: https://cw.fel.cvut.cz/wiki/courses/mpv/start

Knowledge of calculus and linear algebra.

• D. A. Forsyth, J. Ponce. Computer Vision: A Modern Approach. Prentice Hall 2003
• I. Goodfellow, Y. Bengio, A. Courville: Deep Learning, 2016
• A. Torralba, P. Isola, W. T. Freeman: Foundations of Computer Vision,

Lecures, programming exercises, homeworks