Prof. Dr. Mario Vranješ
Prof. Dr. Mario Vranješ received MSc and PhD degrees in electrical engineering from the Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, Croatia in 2006 and 2012, respectively. Today, he works as an associate professor at the same faculty and teaches several different courses in the areas of digital image and video processing, image and video compression, computer vision and machine learning. In his research work, he primarily deals with the field of computer vision and machine learning with an emphasis on automotive applications. So far, he has led more than 10 scientific research projects related to his research areas. As part of his scientific and research work, he has published more than 80 publications in high-ranking international journals and at international conferences where he has presented a large number of papers. Dr. Vranješ is a co-inventor on several international patents. During his scientific career, he visited foreign institutions several times in order to improve his knowledge and skills. Due to the results achieved during his studies and his scientific research work, he was awarded several times as the best student of his home faculty and as the best researcher at his home faculty.
References
- Vajak, Denis; Vranješ, Mario; Grbić, Ratko; Vranješ, Denis: HistWind2 - An Algorithm for Efficient
Lane Detection in Highway and Suburban Environments // IEEE consumer electronics magazine, (2022), 1-9 doi:10.1109/MCE.2022.3171929 - Lukač, Željko; Kaštelan, Ivan; Vranješ, Mario; Todorović, Branislav: AMV ALPHA Learning
Platform for Automotive Embedded Software Engineering // IEEE Transactions on Learning
Technologies, 14 (2021), 3; 292-298 doi:10.1109/TLT.2021.3098505 - 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 (2021) doi:10.1109/MCE.2021.3083206 - Mijić, David; Vranješ, Mario; Grbić, Ratko; Jelić, Borna: Autonomous Driving Solution Based on
Traffic Sign Detection // IEEE consumer electronics magazine (2021) doi:10.1109/MCE.2021.3090950 - Vranješ, Denis; Rimac-Drlje, Snježana; Vranješ, Mario: Adaptive Temporal Frame Interpolation
Algorithm for Frame Rate Up-Conversion // IEEE Consumer Electronics Magazine, 9 (2020), 3; 17-21 doi:10.1109/MCE.2019.2956208
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.