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Computer Vision Methods
B4M33MPV/BE4M33MPV
The Computer Vision Methods course addresses some key challenges in visual perception including how to find images with similar semantic and not always the same visual content (image retrieval), how to pair images (image matching) and how to follow moving objects (visual tracking). The course also discusses some machine learning approaches used in the present computer vision world. You will go through Deep Learning basics, Generative Adversarial Networks (GANs), Invariant descriptors, RANSAC, geometric hashing, several tracking methods, and a few other methods.
The course has no formal prerequisites. However, certain skills and knowledge are assumed, and it is the responsibility of the student to get to the required level.
The assignments are implemented in the Python and numpy computing environment, and familiarity with it will help. The programming assignments, involving either implementing, modifying or testing computer vision methods, are a substantial part of the labs.
Knowledge of the basics of digital image processing as convolution, filtration, intensity transformations, image function interpolations and basic geometric transformations of the image is assumed. Please look at the first lab – Introduction to Image Processing with Pytorch. Knowledge of linear algebra and probability theory is needed to understand the presented computer vision methods.
Details
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FEE CTU
FACULTY
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Summer
TERM
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Metody počítačového vidění
OFFICIAL CZECH NAME
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https://fel.cvut.cz/cz/education/bk/predmety/46/84/p4684506.html
OFFICIAL LINK IN CZECH
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Computer Vision Methods
OFFICIAL ENGLISH NAME
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https://fel.cvut.cz/en/education/bk/predmety/46/85/p4685206.html
OFFICIAL LINK IN ENGLISH
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https://cw.fel.cvut.cz/wiki/courses/mpv/start
Course website
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FEE CTU
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Artificial Intelligence in Robotics
B4M36UIR/BE4M36UIR Winter
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Autonomous Robotics
B3M33ARO1/BE3M33ARO1 Summer
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Computer Vision Methods
B4M33MPV/BE4M33MPV Summer
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Functional Programming
B4B36FUP Summer
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Introduction to Artificial Intelligence
B4B36ZUI Summer
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Julia for optimization and learning
B0B36JUL Winter
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Pattern Recognition and Machine Learning
B4B33RPZ/BE5B33RPZ Winter
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FIT CTU
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FSV CUNI
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MFF CUNI
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Artificial Intelligence I
NAIL069 Winter
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Artificial Intelligence II
NAIL070 Summer
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Deep Learning
NPFL138 Summer
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Deep Reinforcement Learning
NPFL139 Summer
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Dialogue Systems
NPFL123 Summer
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Introduction to Artificial Intelligence
NAIL120 Summer
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Introduction to Machine Learning with Python
NPFL129 Winter
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Large Language Models
NPFL140 Summer
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Modern Algorithmic Game Theory
NOPT021 Winter
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Natural Language Processing
NPFL124 Summer
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