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BE4M33MPV	Computer Vision Methods
Role:	PO, PV, PS, P	Rozsah výuky:	2P+2C
Katedra:	13133	Jazyk výuky:	EN
Garanti:	Matas J.	Zakončení:	Z,ZK
Přednášející:	Čech J., Matas J., Mishkin D., Sattler T., Tolias G.	Kreditů:	6
Cvičící:	Osob je mnoho	Semestr:	L

Webová stránka:

https://cw.fel.cvut.cz/wiki/courses/mpv/start

Anotace:

The course covers selected computer vision problems: search for correspondences between images via interest point detection, description and matching, image stitching, detection, recognition and segmentation of objects in images and videos, image retrieval from large databases and tracking of objects in video sequences. 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.

Cíle studia:

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.

Osnovy přednášek:

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

Osnovy cvičení:

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.

Literatura:

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,

Požadavky:

Knowledge of calculus and linear algebra.

Poznámka:

URL: https://cw.fel.cvut.cz/wiki/courses/mpv/start

Klíčová slova:

registrace obrazů, detekce objektů, sledování

Předmět je zahrnut do těchto studijních plánů:

Plán	Obor	Role	Dop. semestr
MEBIO3_2018	Image Processing	PS	2
MPPRGAI_2025	Před zařazením do oboru	P	2
MEBIO1_2018	Bioinformatics	PV	2
MEKYR_2021	Před zařazením do oboru	PV	2
MEBIO2_2018	Medical Instrumentation	PV	2
MEBIO4_2018	Signal Processing	PV	2
MEOI5_2018	Computer Vision and Image Processing	PO	2

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Stránka vytvořena 19.2.2026 07:50:57, semestry: L/2026-7, L/2025-6, Z/2026-7, Z,L/2027-8, připomínky k informační náplni zasílejte správci studijních plánů

Návrh a realizace: I. Halaška (K336), J. Novák (K336)