Vento Mario

Biography

Mario Vento graduated in Electronics Engineering with praise in 1984 and the PhD in Information Engineering in 1989, both from the University of Naples Federico II. He began his academic career as a researcher in 1990 and since 2003 is an ordinary professor of Information Processing Systems at the University of Salerno (SSD ING-INF05). He has been a member of the Academic Senate from the University of Salerno since 2013 and has been Director of the Department of Information and Electrical Engineering and Applied Mathematics (DIEM) since 2003 and since 2003 he has served as Director of Intelligent Machines for Recognition of Video and Audio Images Mivia Lab). In 2004 he was awarded the "Fellow Scientist" title of the International Association in Pattern Recognition (IAPR) for his significant scientific contributions to "Graph-based Methods in Pattern Recognition", a reward attributed only to 0.25% of the members. From 2002 to 2006 he was elected Chairman of the IAPR TC15 Technical Committee on "Graph Based Representation in Pattern Recognition", and since 2003 has been associated with the Editor of Electronic Letters on Computer Vision and Image Analysis. His research interests are in the areas of Artificial Intelligence, Image Analysis, Automatic Learning, and Artificial Intelligence. In particular, its research activity focuses on the analysis and real-time interpretation of video for traffic monitoring and video surveillance, both statistical and syntactic and structural grading techniques, accurate and incorrect graph matching, classification multi-skilled and learning methodologies for structural descriptions. He has been author of more than 260 research articles in international journals and conferences and is a reviewer of many major journals in Pattern Recognition and Machine Intelligence.

Research Interest

His research interests are in the areas of Artificial Intelligence, Image Analysis, Automatic Learning, and Artificial Intelligence. In particular, its research activity focuses on the analysis and real-time interpretation of video for traffic monitoring and video surveillance, both statistical and syntactic and structural grading techniques, accurate and incorrect graph matching, classification multi-skilled and learning methodologies for structural descriptions.