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Head of subject: Associate Professor László Czúni



Students acquire knowledge related to the following topics, taking into account their individual training plan and interests:


• The concept of machine vision

• Learning and neural mechanisms in biological vision

• Global and local image features

• Detection with Haar Cascades

• Common classification methods: logistic regression, decision trees, SVM

• DNNs and CNNs in recognition and segmentation

• Data annotation and augmentation

• Zero-shot learning and few-shot learning

• Contrastive learning: recommended article

• Adversarial attacks

• Autoencoders

• Attention mechanisms

• Visual transformers

The evaluation is based on the development of an individual project task related to the above topics.



·         Czúni-Tanács: Képi információ mérése, 2011

·         Kató-Czúni: Számítógépes látás, 2011

·         Ian Goodfellow, Yoshua Bengio, Deep Learning (Adaptive Computation and Machine Learning series), ISBN-13: 978-0262035613, The MIT Press

·         Himanshu Singh - Practical Machine Learning and Image Processing For Facial Recognition, Object Detection, and Pattern Recognition Using Python-Apress (2019)

·         Mohamed Elgendy - Deep Learning for Vision Systems (2020, Manning Publications)

·         Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., ... & Krishnan, D. (2020). Supervised contrastive learning. Advances in neural information processing systems, 33, 18661-18673.

·         Rádli, R., & Czúni, L. (2022). Improving the Efficiency of Autoencoders for Visual Defect Detection with Orientation Normalization. In VISIGRAPP (4: VISAPP) (pp. 651-658).

·         Akhtar, N., Mian, A., Kardan, N., & Shah, M. (2021). Advances in adversarial attacks and defenses in computer vision: A survey. IEEE Access, 9, 155161-155196.

·         Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022). Transformers in vision: A survey. ACM computing surveys (CSUR), 54(10s), 1-41.