460-4080 – Image Analysis I (AO1)

The course provides the students with the foundations of image analysis. After passing the course, the student will understand the principles of the selected method of image segmentation and image analysis and will be able to implement them. Graduates of this course will be able to: - Describe selected methods of image segmentation, image analysis, video analysis, and 3D model reconstruction from images. - Describe methods for detecting and classifying objects in images using various types of neural networks. - Design, invent, develop, implement, and test algorithms in the above-mentioned areas. - Assess, evaluate, compare, and recommend algorithms and software products that solve problems in the above-mentioned areas.

The following topics are discussed: Image segmentation, detecting edges, regions, and feature points. Measuring objects for recognition based on features. Classification using discriminant functions, classification based on clustering, classification using neural networks. Using deep neural networks for image analysis. Reconstructing 3D scenes. Analysing 3D point clouds. Processing images varying in time. Object tracking. Recognising actions from video frames. The course includes the computer labs in which the computer programs are realised corresponding to the mentioned topics. Graduates of this course will be able to: - Describe selected methods of image segmentation, image analysis, video analysis, and 3D model reconstruction from images. - Describe methods for detecting and classifying objects in images using various types of neural networks. - Design, invent, develop, implement, and test algorithms in the above-mentioned areas. - Assess, evaluate, compare, and recommend algorithms and software products that solve problems in the above-mentioned areas.

1. Sojka, E., Gaura, J., Krumnikl, M.: Matematické základy digitálního zpracování obrazu, VŠB-TU Ostrava, 2011. 2. Sojka, E.: Digitální zpracování a analýza obrazů, učební texty, VŠB-TU Ostrava, 2000 (ISBN 80-7078-746-5). 3. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, 4th Edition, Pearson, ISBN-13: 9780134734804, 9780133356724, 2018. 4. Simon J.D. Prince: Understanding Deep Learning, 2023, https://anthology-of-data.science/resources/prince2023udl.pdf 5. Szeliski, R.: Computer Vision: Algorithms and Applications, Springer, ISBN 9783030343712, 9783030343729 (eBook), 2022. 6. Gonzalez, R., C., Woods, R., E.: Digital Image Processing, 4th Edition, Pearson, ISBN-13: 9780134734804, 9780133356724, 2018.

1. Burger, W., Burge, M., J.: Principles of Digital Image Processing: Fundamental Techniques, Springer, ISBN-10: 1848001908, ISBN-13: 978-1848001909, 2011 2. Brahmbhatt, S.: Practical OpenCV (Technology in Action), Apress, ISBN-10: 1430260793, ISBN-13: 978-1430260790, 2013 3. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010