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Lecturer(s)
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Paleček Karel, Ing. Ph.D.
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Chaloupka Josef, doc. Ing. Ph.D.
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Course content
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Content of lectures and exercises: 1. Introduction, applications, image acquisition, camera models, color spaces. 2. Detection of points and regions of interest: Harris detector, Laplace operator (LoG), difference of Gaussians (DoG), maximally stable extreme regions (MSER). 3. Descriptors of points and regions of interest: SIFT, SURF, BRIEF, and others. 4. Geometric transformations and their estimation: Hough transform, RANSAC, LMedS. 5. Image comparison, registration, and stitching, and object detection based on local correspondences. 6. Optical flow and its applications. 7. Video analysis, object tracking, the KLT algorithm, and the CamShift and MeanShift methods. 8. Machine learning for computer vision, convolution. 9. Modern architectures of convolutional neural networks, object recognition. 10. Object detection using convolutional networks: single- and two-stage detectors. 11. Image and object segmentation using convolutional networks. 12. Transformers for object detection and segmentation. 13. Introduction to 3D image reconstruction. 14. Reserve.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing)
- Class attendance
- 56 hours per semester
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Learning outcomes
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The subject Machine Vision is focused on student's ability to understand basic principles of computer image processing and recognition.
Theoretic piece of knowledge and practical skills from requered areas
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Prerequisites
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unspecified
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Assessment methods and criteria
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Written exam
Requirements for getting a credit are activity at the seminars. Examination is of the written forms.
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Recommended literature
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DAVIES, E., R.. Computer and Machine Vision, Fourth Edition: Theory, Algorithms, Practicalities.. UK, 2012. ISBN 978-0123869081.
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HLAVÁČ, Václav a Miloš SEDLÁČEK. Zpracování signálů a obrazů. 2. přeprac. vyd.. ČR, 2007. ISBN 978-80-01-03110-0.
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CHALOUPKA, J. Přednášky, cvičení - PVI.
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Raschka, S., Liu, Y., Mirjalili, V., Dzhulgakov, D. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. In Packt Publishing, 2022. ISBN 978-1801819312.
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ŠONKA, Milan, Václav HLAVÁČ a Roger BOYLE. Image processing, analysis, and machine vision. 3rd ed.. Toronto: Thomson, 2008. ISBN 978-0-495-08252-1.
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Ying Liu. Deep Learning Based Image Processing: Recent Advances and Future Trends. In Eliva Press, 2022. ISBN 978-9994982554.
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