Luigi
Di Stefano


Di Stefano
Full Professor of Information Processing Systems University of Bologna Adjunct Faculty

BIO

Luigi Di Stefano is Full Professor at the Department of Computer Science and Engineering (DISI) of the University of Bologna, where he leads the Computer Vision Laboratory (CVLab). Luigi Di Stefano teaches Computer Architecture for undergraduate students and Computer Vision and Image Processing for master students, both at the School of Engineering of the University of Bologna. He also teaches Computer Vision within the Master in Digital Technology Management – Artificial Intelligence Track at the BBS (Bologna Business School).
His research interests are focused on computer vision and machine/deep learning. In these fields, he has coordinated many research projects funded by public grants and private companies and he is author of more than 160 papers in renowned international journals and conferences as well as of several patents. He has published in top-tier journals and conferences such as IEEE PAMI, IJCV, IEEE TIP, CVPR, ICCV, ECCV and NeurIPS. He was a winner of the Runner-Up Paper Award of the 2011 3DIMPVT Conference. He has given invited lectures in PhD schools and many invited talks at academic institution and companies, such as: ETH-Zurich, IIT-Genoa, TUW-Vienna, Huawei, STM, Datalogic, Pirelli, Lamborghini, Ferrari, FCA, SACMI. In 2009-2011 and 2015-2017 he has been a member of the Board of Directors of Datalogic SpA. Since 2011 he is scientific consultant for Pirelli Tyres in the area of computer vision. In 2020 he co-founded the Start-up company eyecan.ai.

COURSES

Computer vision pursues understanding the content of digital images through a large corpus of techniques drawing from a variety of diverse fields. These include, among the others, signal and information theory, calculus, geometry and linear algebra, probability and statistics, optimization, machine learning.

 

Computer vision has emerged through the last three decades as a key process technology to realize inspection and assembly of manufactured goods. Moreover, thanks to recent breakthroughs in image sensing and analysis, we now witness ever-increasing deployment of computer vision within products as widespread as  cars, gaming consoles, apps, personal computers, home appliances.

 

The course will present an overview of some of the most effective computer vision techniques alongside with examples dealing with both “in process” and “in product” applications.

Digital Technology and Innovation Management