AI2FUTURE2022 – 2 days, 40+ moving & inspiring discussion and presentations

13th & 14th of October 2022, Kraš Auditorium, Zagreb, Croatia

Marko Subašić

Assoc. professor

Marko Subašić is conducting research in the field of digital image processing and analysis with applications in medicine, transport, remote sensing, and industry, as well as neural networks, machine learning, and other methods of artificial intelligence. Dr. Subašić is a member of the group for digital image processing at the Faculty of Electrical Engineering and Computing, University of Zagreb. He is a member of the following professional organizations: IEEE (Institute of Electrical and Electronics Engineers) and IEEE Computer Society, the Scientific Center of Excellence for Data Science and Cooperative Systems, the Center of Excellence for Computer Vision, and the Croatian Society for Medical and Biological Engineering.

Dr. Subašić has actively participated in the organization of several international scientific conferences and workshops. He has participated in several scientific projects of the Ministry of Science, the Croatian Science Foundation, competitive EU projects, and commercial projects.



Computer Vision goes shopping – A System for Visual Recognition of Shelved Products

The project’s primary goal could roughly be placed in Retail Store Item Detection, but we are also targeting some additional information related to the products. The additional information should enable automated inspection of product placement, and price checking, but these tasks open a variety of specific technical problems. Fortunately, the essential tool to solve those problems is the same as for store item detection, so we detect them all: shelves, empty spaces, price tags, numerals of the prices, etc. Additional information on the product is also gathered by extending the product detection to detect important product parts, estimate product orientation, and category.

Unsurprisingly, deep neural networks have proven to be the right tool for all tasks, but some very specific modifications to the popular architectures are necessary. Combining many years of experience in the industry and innovative approaches, Megatrend and Ph.D. students at the Faculty of Electrical Engineering and Computing created the perfect team to tackle these problems.