Image Analysis II

Image Analysis II
460-4107/01
čeština Image Analysis II
460-4107/02
angličtina

Summary

The following topics are covered: Modern methods of object detection and object recognition. Typically, the approaches are based on the image descriptors that are combined with the machine learning methods. The principles and aplications of deep learning and convolutional neural networks are also covered (detection of vehicles, pedestrians, faces).

Literature

1. Lakshmanan, Valliappa, et al. Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images. O’Reilly Media, 2021.
2. Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into Deep Learning, https://d2l.ai, 2020
3. Chollet, F.: Deep Learning with Python. Manning, , 2017

Advised literature

1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, , , 2011
2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, , , 2013
3. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017

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Language of instruction	čeština, angličtina
Code	460-4107
Abbreviation	ANO II
Course title	Image Analysis II
Coordinating department	Department of Computer Science
Course coordinator	Ing. Radovan Fusek, Ph.D.