Multimodal Scene Understanding: Algorithms, Applications and Deep Learning

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In. Bestandsnummer des Verkäufers ria9780128173589_new

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Über die Autorinnen und Autoren: He is Assistant Professor with University of Twente (the Netherlands), heading a group working on scene understanding. He received the PhD degree (summa cum laude) from University of Bonn (Germany) in 2011. His research interests are in the fields of computer vision and photogrammetry with specialization on scene understanding, deep learning, UAV vision, and multi-sensor fusion. He published over 90 articles in international journals and conference proceedings. He serves as co-chair of ISPRS working group II/5 Dynamic Scene Analysis, and recipient of the ISPRS President's Honorary Citation (2016) and Best Science Paper Award at BMVC 2016. Since 2016, he is a Senior Member of IEEE. He is regularly serving as program committee member of conferences and reviewer for international journals.

His works received several awards, including a DAGM-Prize 2002 , Dr.-Ing. Siegfried Werth Prize 2003, DAGM-Main Prize 2005, IVCNZ best student paper award , DAGM-Main Prize 2007, Olympus-Prize 2007, ICPRAM Best student paper award 2014, ICMC Best student paper award 2014, the WACV 2015 Challenge Award, the Günter Enderle Award (Eurographics) 2017 and the CVPR 2017 Multi-Object Tracking Challenge. In 2011, the European Commission awarded Bodo Rosenhahn with a 1.43 million Euros ERC-Starting Grant and in 2013 with a POC Grant. He published more than 180 research papers, journal articles and book chapters, holds more than 10 patents and edited several books.

Full professor at the University of Verona, Italy, and director of the PAVIS (Pattern Analysis and Computer Vision) department at the Istituto Italiano di Tecnologia. He took the Laurea degree in Electronic Engineering in 1989 and a Ph.D. in Electronic Engineering and Computer Science in 1993 at the University of Genova, Italy.
His main research interests include: computer vision and pattern recognition/machine learning, in particular, probabilistic techniques for image and video processing, with applications on video surveillance, biomedical image analysis and bioinformatics.