Computer Vision Methods


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.




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