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Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +5 more

Narvika Bovcon is a Full Professor at the Faculty of Computer and Information Science, specializing in video and new media art. Since 2016, she has served as editor-in-chief of the journal Art Words and has authored numerous new media art projects and exhibitions. Her academic activities include teaching courses such as Introduction to Graphics Design, Graphic Design, and Multimedia Content. Research Focus: Bovcon's work explores interdisciplinary intersections of technology and art, with emphasis on: Digital animation and mixed reality applications User interface design for cultural heritage Augmented reality in fine arts and literature Digital preservation methodologies Computational approaches to visual communications Publication Trends: Her recent scholarly output (2013-2021) demonstrates strong focus on digital heritage preservation, augmented reality implementations in cultural contexts, computational analysis of art, and educational applications of interactive media. Works frequently employ case studies and technical evaluations within Slovenian cultural frameworks. Research Projects: P2-0214 Computer Vision (ARRS, 2019-2024) - Core research programme J7-3158 Sustainable digital preservation of Slovenian new-media art (ARRS, 2021-2024) ŠIPK 2 Cultural Heritage documentation using new technologies (Structural Funds, 2018) PKP 1 Interactive computer installation integration (Structural Funds, 2014) Laboratory Affiliation: Active member of the Computer Vision Laboratory at the Faculty of Computer and Information Science, contributing to technical-artistic research initiatives.

• new media art
• visual communications
• digital animation
• +2 more

Domen Tabernik is a researcher at the Visual Cognitive Systems Laboratory . His work focuses on advanced computer vision and deep learning applications, particularly in robotics, textile inspection, and multimodal image analysis for anomaly detection. Current projects include RTFM (Robot Textile/Fabric Inspection), RoDEO (Robust Deep Learning for Earth Observation), and MUXAD (Multimodal Image Understanding). Past projects span computer vision for content-based retrieval, factory automation tools (GOSTOP), and deep learning for visual data inconsistency detection.

• Computer Vision
• Deep Learning
• Robotics
• +3 more

Matej Kristan is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, where he serves as Vice Chair of the Department of Artificial Intelligence . He leads the Visual Object Tracking (VOT) Initiative , presides over the IAPR Slovenian Pattern Recognition Society , and acts as Associate Editor for IJCV . PhD from Faculty of Electrical Engineering, UL (2008) Co-organized 13+ workshops/conferences Research Interests focus on: Visual Object Tracking : Long-term re-detection, transparent object tracking, segmentation-based methods Autonomous Boats : Obstacle detection, sensor fusion, maritime navigation Physics-Informed Deep Learning : Sea level forecasting (HIDRA models), climate prediction Surface Defect Detection : Real-time deep architectures for cracks/dents Scientific Excellence : 26 Research Excellence Awards (Slovenian Research Agency) 10 Teaching Excellence Awards (UL/FRI) Best Paper Awards: ISPA2015, BMVC2022, Pattern Recognition Journal 2024 Student Mentorship : Supervised 8 PhD students including Alan Lukežič (summa cum laude 2021), Domen Tabernik , and Peter Uršič . His students have won 23 awards including multiple Prešeren Awards and Uroš Seljak distinctions. Laboratory Involvement : Key member of the Visual Cognitive Systems Laboratory , contributing to multimodal perception systems for maritime robotics, educational robotics platforms, and advanced deep learning architectures.

• Computer Vision
• Visual Object Tracking
• Detection and Segmentation
• +3 more

Luka Šajn is an Assistant Professor at the University of Ljubljana , affiliated with the Computer Vision Laboratory . His work bridges computer science , machine learning , and medical imaging , focusing on automated analysis of medical data. Academic Rank: Assistant Professor University: University of Ljubljana Laboratory: Computer Vision Laboratory Šajn's research centers on multi-resolution pattern parametrization and its applications in medical diagnostics. His projects include automated segmentation of whole-body bone scintigrams , detection of white spot lesions in dentistry, and 3D documentation of cultural heritage using computer vision. Šajn's publications (2005–2020) span texture classification , image segmentation , and medical AI . Key subfields include dental diagnostics , coronary disease analysis , mobile vision systems , and ultra-wideband trajectory modeling .

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +3 more

Roman Kamnik is a Full Professor at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia, where he serves as a Higher Education Teacher and is affiliated with the Laboratory for Robotics. His academic position encompasses teaching, research, and leadership in advanced engineering disciplines within the university's primary technical faculty. Professor Kamnik's research program spans critical domains in modern engineering, with emphasis on: Robotics and manufacturing systems integration Embedded systems for industrial automation Intelligent mobile transport solutions Biomechanics applications (Module C) Electro-mechanical product design Advanced robotic measurement technologies His work bridges theoretical innovation with practical implementation, particularly through the Laboratory for Robotics where he develops cutting-edge solutions for automation and intelligent systems. The research demonstrates consistent focus on real-world applications in manufacturing, transportation, and biomedical engineering contexts.

• Robotics
• Embedded Systems
• Automation
• +4 more

Marko Munih is a full professor at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia. He serves as a higher education teacher and leads the Laboratory for Robotics, contributing to advancements in automation and robotic systems. Research interests: Robotics, Embedded Systems in Automation, Microcomputer Systems, Robot Kinematics and Dynamics, Robotic Mechanisms, and Measurement Systems. His work bridges theoretical and applied aspects of mechatronics and computer science. Labs & Teams: Associated with the Laboratory for Robotics at the Faculty of Electrical Engineering, focusing on robot design, control systems, and practical applications.

• Robotics
• Embedded Systems in Automation
• Microcomputer Systems
• +5 more

Aleš Jaklič is an Assistant Professor affiliated with the Computer Vision Laboratory . His work spans computer vision, 3D reconstruction, and educational technologies, with a focus on practical applications in archaeology, meteorology, and STEM-C education. Research interests include: 3D modeling from point clouds Superquadric parameter prediction from depth images IoT-based educational tools for computer science Historical artifact digitization Image processing algorithms His publications since 2000 demonstrate expertise in geometric modeling, computer vision, and educational technology. Highlights include work on superquadric recovery (2000, 2003), archaeological modeling (2015), and IoT education frameworks (2020). Recent research (2021) explores neural network approaches to depth image analysis. Active in research programs funded by the Slovenian Research Agency (ARRS) since 2009, including the ongoing P2-0214 - Computer Vision program (2019-2024). He has also contributed to the ŠIPK 5 project on IoT education (2020).

• Computer Vision
• 3D Reconstruction
• Machine Learning
• +3 more

Alan Lukežič serves as an Assistant Professor and laboratory member at the Visual Cognitive Systems Laboratory (Laboratorij LUVSS), where he teaches Advanced topics in computer vision. His academic work bridges visual perception systems and computational cognition within the broader field of computer science. His research focuses on Computer Vision, Cognitive Systems, Artificial Intelligence, and Image Processing, exploring machine interpretation of visual data for applications in robotics, medical diagnostics, and human-computer interfaces. This interdisciplinary work integrates neural modeling with real-world visual system design. As part of the Visual Cognitive Systems Laboratory team in room R2.37, he contributes to experimental frameworks analyzing how artificial systems emulate biological visual cognition through computational architectures.

• Computer Vision
• Cognitive Systems
• Artificial Intelligence
• +1 more

Jon Natanael Muhovič is an Assistant Professor affiliated with the Visual Cognitive Systems Laboratory . His research focuses on machine perception , development of intelligent systems , and robotics , leveraging advanced computational methods to address complex visual data challenges. Key research themes include deep learning applications for inconsistency detection (ARRS project J2-9433, 2018-2021), adaptive sensing techniques for autonomous vehicles (ARRS project J2-2506, 2020-2023), and data-driven frameworks for machine vision (ARRS project L2-3169, 2021-2024). He has also contributed to practical initiatives such as the feasibility study of a navigation system for the blind (2021) and software development for classification tasks (2018). As a member of the Laboratory for Underwater Vision and Signal Processing Systems (LUVSS) , his work bridges theoretical innovation with real-world applications in visual cognition and machine perception.

• Machine Learning
• Computer Science
• Neural Networks
• +3 more