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Lorenzo Seidenari is an Assistant Professor at the University of Florence. Previously he was a Postdoc researcher at Media Integration and Communication Center of the University of Florence. He received his Ph.D. degree in computer engineering in 2012 from the University of Florence. His research focuses on object and action recognition in video and images using deep learning. On this topics he addressed RGB-D activity recognition, embedding learning for multimodal-fusion, anomaly detection in video and people behavior profiling. He was a visiting scholar at the University of Michigan in 2013. He organized and gave a tutorial at ICPR 2014 on image categorization.
He has been in the organizing committee of ACM MM 2010, ECCV 2012, CBMI 2017. He is a reviewer for several international conferences: CBMI, ICIAP, ACM MM, CAIP, CIARP, IEEE CVPR, IEEE ECCV, IEEE ICME, IAPR ICPR, WIAMIS, ICVS, AVSS. He frequently serves as a reviewers for several prestigious journals: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Human Machine Systems (THMS), IEEE Transactions on Circuit and Systems for Video Technology(TCSVT), IEEE Transactions on Cybernetics (TCYB), ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Pattern Recognition Letters (PRL), Computer Vision and Image Understanding (CVIU), Pattern Recognition (PR), Multimedia Tools and Applications (MTAP), Pattern Analysis and Applications (PAAA)
He was an Adjunct Professor for the course of Digital Circuits at the Polo Universitario Aretino – Politecnico di Milano. He has taught a PhD level class at the Smart Computing and Computer Engineering PhD Schools on image recognition. He advised, 3 Phd, 8 Msc and 5 Bsc students.
He is author of 10 journal papers and more than 30 peer-reviewed conference papers.
His scientific impact indices are the following:
- Google Scholar, 805 Citations, H-Index: 15
- Scopus 453 Citations, H-Index 10.
Computer Vision; Machine Learning; Multimedia Analysis