Probabilistic and Biologically Inspired Feature Representations

Michael Felsberg received the Ph.D. in engineering from the University of Kiel, Germany, in 2002. Since 2008, he has been a Full Professor and the Head of the Computer Vision Laboratory at Linkoping University, Sweden. His current research interests include signal processing methods for image analysis, computer and robot vision, and machine learning. He has published more than 150 reviewed conference papers, journal articles, and book contributions. He was a recipient of awards from the German Pattern Recognition Society in 2000, 2004, and 2005, from the Swedish Society for Automated Image Analysis in 2007 and 2010, from the Conference on Information Fusion in 2011 (Honorable Mention), from the CVPR Workshop on Mobile Vision 2014, and from the ICPR 2016 track on Computer Vision (Best Paper). He has achieved top ranks on various challenges (VOT: 3rd 2013, 1st 2014, 2nd 2015, 1st 2016, 1st 2017 (sequestered test); VOT-TIR: 1st 2015, 1st 2016, 3rd 2017; OpenCV Tracking: 1st 2015; KITTI Stereo Odometry: 1st 2015, March). He has coordinated the EU projects COSPAL and DIPLECS and has been an Associate Editor of the Journal of Mathematical Imaging and Vision and the Journal of Image and Vision Computing. He was Publication Chair of the International Conference on Pattern Recognition 2014 and served as Track Chair in 2016, has been a VOT-committee member since 2015, was the General Co-Chair of the DAGM symposium in 2011 and General Chair of CAIP 2017,and will be Area Chair at ECCV 2018 and Program Chair of SCIA 2019.

Preface.- Acknowledgments.- Introduction.- Basics of Feature Design.- Channel Coding of Features.- Channel-Coded Feature Maps.- CCFM Decoding and Visualization.- Probabilistic Interpretation of Channel Representations.- Conclusions.- Bibliography.- Author's Biography.- Index.