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Lecturer(s)
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Skalický Erich, Ing. Ph.D.
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Straka Zdeněk, Ing. Ph.D.
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Korotvička Emil, Ing. Ph.D.
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Obst David, Ing. Ph.D.
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Course content
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Advanced methods of representation of image information. Local descriptors in the image, classification in computer vision. Convolutional Neural Networks, detection neural networks, metric learning and image segmentation via neural netoworks, Transformer model in computer vision, generative neural networks, projective geometry, 3D reconstruction.
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Learning activities and teaching methods
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Lecture, Practicum
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| prerequisite |
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| Knowledge |
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| Analysis and interpretation of informations, algorithmization and implementation of tasks, assessment of achieved results. |
| Python programming, basics of digital image processing, working with data. |
| learning outcomes |
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| Ability to analyze, research, and generalize presented principles, describe problem domain, compile algorithmization |
| ability to train, fine-tune, and evaluate a neural network, solve tasks of classification, regression, and segmetaition of an image, and object detection. |
| teaching methods |
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| Lecture |
| Practicum |
| assessment methods |
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| Oral exam |
| Skills demonstration during practicum |
| Project |
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Recommended literature
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Šonka, Milan; Hlaváč, Václav. Počítačové vidění. Praha : Grada, 1992. ISBN 80-85424-67-3.
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