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Image Analysis II

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

Summary

The following topics are particularly discussed: modern methods for object detection and recognition in images, principles of deep learning combined with image analysis, current variants of convolutional neural networks and their practical applications for different types of objects. The subject will also include the theme of generative networks.

Upon successful completion of the course, the student will be able to:
- apply modern methods of image analysis for object detection and recognition in real-world environments,
- evaluate and analyze the strengths and weaknesses of individual methods,
- design and modify convolutional neural network models,
- assess the robustness of the models,
- apply these models to practical tasks, such as face recognition, pedestrian localization, or vehicle detection in images,
- use generative networks to augment datasets,
- generate image samples.

Literature

1. Ayyadevara, V. Kishore; Reddy, Yeshwanth. Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications. Packt Publishing, 2020. ISBN 978-1839213472 .
2. Lakshmanan, V.; Shlens, J.; Sukthankar, R. Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images. O’Reilly Media, 2021. ISBN 978-1098102364.
3. Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning. arXiv, 2021. Available at: https://doi.org/10.48550/arXiv.2106.11342.

Advised literature

1. Burger, W.; Burge, M. J. Principles of digital image processing: Fundamental techniques. Springer, 2011. ISBN 978-1848001909 .
2. Chollet, F. Deep learning with Python. Manning, 2017. ISBN 978-1617294433.
3. Howse, Joseph; Minichino, Joe. Learning OpenCV 4 Computer Vision with Python 3. 3rd ed. Birmingham: Packt Publishing, 2020. ISBN 978-1789531619.


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Language of instruction čeština, angličtina
Code 460-4107
Abbreviation AO2
Course title Image Analysis II
Coordinating department Department of Computer Science
Course coordinator Ing. Radovan Fusek, Ph.D.