Computer Vision Systems Group
Info
The Computer Vision Systems Group focuses its activities on advanced methods in computer vision and multimodal data analysis, developing algorithms that enable semantic image interpretation, integration of heterogeneous data sources (including satellite data, LiDAR, 3D imaging, and temporal data), as well as their processing and visualization in 2D and 3D environments. The research includes, among others, object detection and segmentation, registration of visual data, and the development of scalable machine learning models and decision support systems, supported by optimization methods and physics-inspired approaches. Particular emphasis is placed on applications in complex systems with high structural uncertainty, such as transport, environmental, and diagnostic systems (including biomedical applications).
An important element of the group’s activity is international collaboration. The team works with researchers from Halmstad University on AI models inspired by brain neuroplasticity, developed for continual and lifelong learning. Collaboration is also being developed with the University of Žilina, focusing on applications of optimization and machine learning methods (including computer vision) in real-world transport systems. Team members also collaborate with the Wigner Research Centre for Physics in Budapest in the field of advanced physics-inspired computational methods and optimization.
Among the group’s key achievements are the development of novel methods for multimodal data integration and analysis, as well as tools for 3D visualization. The team actively participates in international research projects, including the European Q-Fence project funded under the Horizon Europe program, within which post-quantum cryptographic methods are being developed to ensure secure processing and transmission of sensitive data (including medical, financial, and satellite data). The conducted work combines fundamental research with practical applications, particularly in the areas of data security, environmental analysis, and modern transport systems.
The Computer Vision Systems Group has existed since 1987. The group’s co-founder and long-time head was Dr. Eng. Ryszard Winiarczyk. The Computer Vision Systems Group is now led by Associate Professor Krzysztof Domino.
Publications
2020
- Tomaka A.; Tarnawski M.; Luchowski L.; Cerkaski B.; Pojda D.; Integracja obrazowania wielomodalnego w zastosowaniu do wspomagania diagnostyki stomatologicznej, planowania i oceny wyników leczenia; Inżynieria Biomedyczna - podstawy i zastosowania; Tom 8 - Obrazowanie Biomedyczne; 2020
- Luchowski L.; Tomaka A.; Pojda D.; Tarnawski M.; Skabek K.; Kowalski P.; Skanery3D - trójwymiarowe obrazowanie powierzchni; Inżynieria Biomedyczna - podstawy i zastosowania; Tom 8 - Obrazowanie Biomedyczne; 2020
- Domino K.; Multivariate cumulants in outlier detection for financial data analysis; Physica A: Statistical Mechanics and its Applications; 2020
2019
- Tarnawski M.; Tomaka A.; Luchowski L.; Zastosowanie cyfrowych modeli gnatostatycznych. Opis przypadku / The application of digital gnathostatic models. Case report; In A. Machorowska-Pieniążek (ed.): Ortodoncja w praktyce. Teksty wybrane. Tom 1/2; 2019
- Kruszewska N.; Bełdowski P.; Domino K.; Lambert K.; Investigating conformation changes and network formation of mucin in joints functioning in human locomotion; In A.Gadomski (ed.): Multiscale Locomotion: Its Active-Matter Addressing Physical Principles; 2019
- Tomaka A.; Luchowski L.; Pojda D.; Tarnawski M.; Domino K.; The dynamics of the stomatognathic system from 4D multimodal data; In A.Gadomski (ed.): Multiscale Locomotion: Its Active-Matter Addressing Physical Principles; 2019
- Tomaka A.; Luchowski L.; Pojda D.; Skabek K.; Tarnawski M.; Applying computational geometry to designing an occlusal splint; Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (TCIV); 2019
- Tomaka A.; Luchowski L.; Pojda D.; Tarnawski M.; Dynamic Occlusion Surface Estimation from 4D Multimodal Data; Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing; 2019
- Domino M.; Domino K.; Tomasz J.; Pawlinski B.; Gajewski Z.; The hierarchy of the features of uterine myoelectrical activity during estrus cycle in sows; 52nd Annual Conference of Physiology and Pathology of Reproduction; 2019
- Pojda D.; Tomaka A.; Luchowski L.; Skabek K.; Tarnawski M.; Applying computational geometry to designing an occlusal splint; Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2018. Lecture Notes in Computer Science; 2019
- Domino K.; Gawron P.; An algorithm for arbitrary–order cumulant tensor calculation in a sliding window of data streams; International Journal of Applied Mathematics and Computer Science; 2019
2018
- Tomaka A.; Pojda D.; Luchowski L.; Tarnawski M.; Methods of Tooth Equator Estimation; Computer and Information Sciences; 2018
- Głomb P.; Romaszewski M.; Cholewa M.; Domino K.; Application of hyperspectral imaging and Machine Learning methods for the detection of gunshot residue patterns; Forensic Science International; 2018
- Blachowicz T.; Andreychouk V.; Domino K.; Random walk analysis of cave maps for exemplary gypsum caves- mazes of Western Ukraine; Landform Analysis ; 2018
- Domino K.; Gawron P.; Pawela Ł.; Efficient computation of higher order cumulant tensors; SIAM J. SCI. COMPUT.; 2018
- Domino K.; Glos A.; Ostaszewski M.; Pawela Ł.; Sadowski P.; Properties of quantum stochastic walks from the asymptotic scaling exponent; Quantum Information and Computation; 2018
2017
- Domino K.; Glos A.; Ostaszewski M.; Superdiffusive quantum stochastic walk definable on arbitrary directed graph; Quantum Information & Computation; 2017