Computer Vision Methods
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BE4M33MPV | Z,ZK | 6 | 2P+2C | English |
- Relations:
- During a review of study plans, the course B4M33MPV can be substituted for the course BE4M33MPV.
- During a review of study plans, the course AE4M33MPV can be substituted for the course BE4M33MPV.
- During a review of study plans, the course A4M33MPV can be substituted for the course BE4M33MPV.
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has already completed the course B4M33MPV (mutually exclusive courses).
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has already completed the course AE4M33MPV (mutually exclusive courses).
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has already completed the course A4M33MPV (mutually exclusive courses).
- In order to register for the course BE4M33MPV, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has previously completed the course B4M33MPV (mutually exclusive courses).
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has previously completed the course AE4M33MPV (mutually exclusive courses).
- It is not possible to register for the course BE4M33MPV if the student is concurrently registered for or has previously completed the course A4M33MPV (mutually exclusive courses).
- Course guarantor:
- Jiří Matas
- Lecturer:
- Jan Čech, Jiří Matas, Dmytro Mishkin, Torsten Sattler, Georgios Tolias
- Tutor:
- Georgios Kordopatis-Zilos, Jiří Matas, Dmytro Mishkin, Lukáš Neumann, Jonáš Šerých, Pavel Šuma
- Supervisor:
- Department of Cybernetics
- Synopsis:
-
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.
- Requirements:
-
Knowledge of calculus and linear algebra.
- Syllabus of lectures:
-
1. Correspondences and wide baseline stereo I. Motivation and applications. Perspective pinhole camera model.
Interest point and distinguished regions detection: Harris detector (corner detection)
2.Correspondences and wide baseline stereo II. Laplace operator and its approximation by difference of Gaussians. Affine covariant version. Descriptors of SIFT (scale invariant feature transform), RootSIFT.
Multiview feature matching. Deep learned features: R2D2, Super Glue.
3.RANSAC (Random Sample and Consensus)
4.3D reconstruction I.
5.3D reconstruction II
6.Deep learning I. Convolutional Neural Networks, Transformers. Architectures for image recognition.
7.Deep learning II. Architectures for object detection and semantic segmentation.. Foundation models (CLIP, DINO, Segment Anything, Depth Anything)
8.Tracking I. Problem formulation. KLT - Lucas-Kanade tracker, DCF - discriminative correlation tracker.
9.Tracking II. Long-term tracking.
10.Image Retrieval I., Bag-of-Words, VLAD, spatial verification, special objectives: zoom in/out .
11.Image Retrieval II. deep metric learning, architectures, losses
12.Self-supervised representation learning. Auto-encoders, learning via augmentations, contrastive approaches
13.Generative modelling for Computer Vision
- Syllabus of tutorials:
-
1.Introduction to Image Processing in python using PyTorch.
2.Debugging pytorch.
3.Correspondence problem I, detection of the interest points.
4.Correspondence problem II, computing local invariant description.
5.Correspondence problem III, finding tenative correspondences and RANSAC.
6.Correspondence problem, summary.
7.Convolutional Neural Networks: training a classifier.D LN recording 2022
8.Convolutional Neural Networks II: debugging training process.
9.Image Retrieval, BoW TF-IDF, fast spatial verification.
10.Assignment defence.
11.Deep metric learning.
12.Self-supervised Learning.
13.Tracking.
- Study Objective:
-
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.
- Study materials:
-
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,
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/mpv/start
- Time-table for winter semester 2025/2026:
- Time-table is not available yet
- Time-table for summer semester 2025/2026:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - The course is a part of the following study plans:
-
- Open Informatics - Computer Vision and Image Processing (compulsory course of the specialization)
- Medical Electronics and Bioinformatics - Specialization Image Processing (PS)
- Medical Electronics and Bioinformatics - Specialization Signal Processing (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Bioinformatics (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)
- Cybernetics and Robotics (compulsory elective course)
- prg.ai Master (compulsory course in the program)