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
2026
- Pojda D.; Domino K.; Tarnawski M.; Tomaka A.; Transformation-driven generation of comparable projection images from multimodal anatomical scenes; Submitted: Journal of Computational Science (preprint available on arXiv); 2026
- Macek W.; Pojda D.; Podulka P.; Mourao A.; Correia J.; Zhu S.; Qualitative and quantitative fractography of structural steel S235; Submitted: w recenzji; 2026
- Tomaka A.; Domino K.; Tarnawski M.; Pojda D.; Quantifying mandibular positioning error and simulated temporomandibular joint-space changes in patient-specific occlusal splints; Submitted: Medical & Biological Engineering & Computing (preprint available on arXiv); 2026
- Kędziera E.; Gamon W.; Koniorczyk M.; Mzaouali Z.; Galadíková A.; Domino K.; EMU circulation planning for Silesian Railways: case study and a quantum approach; Submitted: arxiv preprint
2026
- Macek W.; Korpyś M.; Pojda D.; FRASTA-toolbox: An Open Source Tool for Fracture-surface Analysis; SoftwareX; 2026
- Doucet E.; Mzaouali Z.; Robertson R.; Gardas B.; Deffner S.; Domino K.; Thermodynamic significance of QUBO encoding on quantum annealers; New Journal of Physics; 2026
- Żarski M.; Nowaczyk S.; Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting; IEEE Access; 2026
2025
- Domino K.; Gamon W.; Od Baltimore do Śląska: komputery kwantowe i hybrydowe metody optymalizacji w planowaniu transportu szynowego ; Transport Miejski i Regionalny; 2025
- Zawadzka A.; Żarski M.; Drejer K.; Głomb P.; Romaszewski M.; Cholewa M.; Map-Guided Cross-Training for Building Detection; Geoscience and Remote Sensing Letters; 2025
- Okoniewska M.; Sionkowski P.; Kruszewska N.; Błażejczyk K.; Domino K.; A Scalable and Automated Recurrence Plot Method for Detecting Climate Change: The Case of the UTCI Bioclimatic Index in Central Europe; IEEE Transactions on Geoscience and Remote Sensing; 2025
- Żarski M.; Nowaczyk S.; Balancing Performance and Scalability of Demand Forecasting ML Models; Intelligent Data Analysis XXIII [IDA]; 2025
- Tomaka A.; Luchowski L.; Tarnawski M.; Pojda D.; Computer-Aided Design of Personalized Occlusal Positioning Splints Using Multimodal 3D Data; Computer Vision and Image Understanding; 2025
- Robertson R.; Doucet E.; Mzaouali Z.; Domino K.; Gardas B.; Deffner S.; Simon's Period Finding on a Quantum Annealer; 2025 IEEE International Conference on Quantum Computing and Engineering (QCE); 2025
- Luchowski L.; Pojda D.; Visualization of a multidimensional point cloud as a 3D swarm of avatars; Applied Sciences; 2025
- Tomaka A.; Pojda D.; Tarnawski M.; Luchowski L.; Transformation trees – documentation of multimodal image registration; Computers in Biology and Medicine; 2025
- Pojda D.; Żarski M.; Tomaka A.; Luchowski L.; dpVision: environment for multimodal images; SoftwareX; 2025
- Domino K.; Sochan A.; Miszczak J.; Analytical assessment of workers' safety concerning direct and indirect ways of getting infected by dangerous pathogen; Journal of Computational Science; 2025
- Domino K.; Doucet E.; Robertson R.; Gardas B.; Deffner S.; On the Baltimore Light RailLink into the quantum future; Scientific Reports; 2025
- Gawlak K.; Konieczny J.; Domino K.; Miszczak J.; Statistical analysis of geoinformation data for increasing railway safety; Journal of Rail Transport Planning & Management; 2025
- Koniorczyk M.; Krawiec K.; Botelho L.; Bešinović N.; Domino K.; Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach; Journal of Rail Transport Planning & Management; 2025