Prof. Dr. Ratko Grbić
Prof. Dr. Ratko Grbić received his M.S. and Ph.D. degrees in 2005 and 2013, respectively, both from the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Croatia. He is currently working as an Associate Professor at the same institution here he teaches courses related to machine learning, internet of things and embedded systems. His research interests include machine learning, especially in the fields of image processing and computer vision, robotic vision, and automotive-related applications. As part of his scientific and research work, he has actively contributed to 10 research projects, co-invented 4 international patent applications, and authored 25 publications in international journals, along with 47 conference papers. Dr. Grbić's contributions have been recognized with awards, he was the best student in the third year of his profile at his home faculty and the best young researcher at his home faculty in 2014. As a co-author, he received the Special Merit Award and Best Paper Award at international conference Zooming Innovation in Consumer Electronics (ZINC) in 2021.
References
Grbić, Ratko ; Koch, Brando
Automatic vision-based parking slot detection and occupancy classification // Expert systems with applications, 225 (2023), 120147, 14. DOI: 10.1016/j.eswa.2023.120147
Džijan, Matej ; Grbić, Ratko ; Vidović, Ivan ; Cupec, Robert
Towards fully synthetic training of 3D indoor object detectors: Ablation study // Expert systems with applications, 232 (2023), 120723, 12. DOI: 10.1016/j.eswa.2023.120723
Vajak, Denis ; Vranješ, Mario ; Grbić, Ratko ; Vranješ, Denis
HistWind2 - An Algorithm for Efficient Lane Detection in Highway and Suburban Environments // IEEE consumer electronics magazine, 12/2023 (2023), 5; str. 45-52. DOI: 10.1109/MCE.2022.3171929
Mijić, David ; Vranješ, Mario ; Grbić, Ratko ; Jelić, Borna
Autonomous Driving Solution Based on Traffic Sign Detection // IEEE consumer electronics magazine, 12/2023 (2023), 5; str. 39-44. DOI: 10.1109/MCE.2021.3090950
Jelić, Borna ; Grbić, Ratko ; Vranješ, Mario ; Mijić, David
Can we replace real-world with synthetic data in deep learning-based ADAS algorithm development? // IEEE consumer electronics magazine, 12/2023 (2023), 5; str. 32-38. DOI: 10.1109/MCE.2021.3083206
Courses
Gain an in-depth understanding of the features of digital images and video signals and how to process images with suitable algorithms.
Understand how a digital image is presented, what are the preprocessing and image manipulation procedures, how to perform image segmentation, how to detect different contours and objects, and how to track detected objects. Gain an in-depth understanding of the features of digital images and video signals and how to process images with suitable algorithms. Finally, gain an overview of different computer vision-based advanced ADAS algorithms and practical guidelines for constructing them.
Course Topics:
- Digital Image Presentation
- Techniques for image preprocessing and image manipulation
- Geometric image transformations
- Edge detection
- Image segmentation and contour detection
- Object detection
- Definition of object detection and challenges in object detection
- Motion analysis and object tracking
- CV-based advanced ADAS algorithms
Requirements
Software: Open CV, Chrome browser.
Hardware: Computer with an Internet connection, working speakers, and microphone.
Prior knowledge: Students should have basic knowledge of Python language, being able to write simple programs.