Issue No. 08 - August (2009 vol. 31)
We are happy to announce the appointment to the TPAMI Editorial Board of Daniel Cremers, Andrew Fitzgibbon, and David Forsyth. Professor Cremers will handle papers in optimization, variational methods, and statistical vision. Dr. Fitzgibbon will be in charge of submissions in geometry, vision for graphics, and machine learning. Professor Forsyth will be responsible for papers in object recognition, motion, and machine learning.
Brief biographies appear below. Welcome aboard, and thank you in advance for all of your hard work!
Ramin Zabih, Editor-in-Chief
Jiri Matas, Associate Editor-in-Chief
Zoubin Ghahramani, Associate Editor-in-Chief
Daniel Cremers received bachelor’s degrees in mathematics (1994) and physics (1994) and the master’s degree in theoretical physics (1997) from the University of Heidelberg. In 2002, he received the PhD degree in computer science from the University of Mannheim, Germany. Subsequently he spent two years as a postdoctoral researcher at the University of California at Los Angeles (UCLA) and one year as a permanent researcher at Siemens Corporate Research in Princeton, New Jersey. Since 2005, Professor Cremers has headed the Research Group for Computer Vision, Image Processing, and Pattern Recognition at the University of Bonn, Germany. His areas of interest include statistical methods, continuous and discrete optimization, variational methods and partial differential equations, Markov random fields, and graph-theoretic approaches. Application areas include image segmentation, statistical shape analysis, motion estimation, 3D reconstruction, and tracking. His work has received several awards, including the Best Paper of the Year 2003 (International Pattern Recognition Society), the 2004 Olympus Award, and the 2005 UCLA Chancellor’s Award for Postdoctoral Research. Professor Cremers has served as area chair and a program committee member for the main computer vision conferences. Over the past few years, he has given more than 120 talks and invited seminars.
Andrew Fitzgibbon studied mathematics and computer science at University College Cork, then at Heriot-Watt University and Edinburgh University, from which he received the PhD degree in 1997. He is a senior researcher at Microsoft Research, Cambridge, United Kingdom. His research interests are in the intersection of computer vision and computer graphics, with excursions into neuroscience. Recent papers have been on the recovery of 3D geometry from 2D images, general-purpose camera calibration, human 3D perception, and the application of natural image statistics to problems of figure/ground separation and new-view synthesis. He has twice received the IEEE’s Marr Prize and software he wrote won an Engineering Emmy Award in 2002 for significant contributions to the creation of complex visual effects. Until June 2005, he held a Royal Society University Research Fellowship at Oxford University’s Department of Engineering Science.
David Forsyth is currently a full professor at the University of Illinois at Urbana-Champaign, where he recently moved from the University of California, Berkeley, where he was also a full professor. He has published more than 130 papers on computer vision, computer graphics, and machine learning. He served as program cochair for IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in 2000, general cochair for CVPR 2006, and program cochair for the European Conference on Computer Vision 2008, and is a regular member of the program committees of all major international conferences on computer vision. He served four years on the ACM SIGGRAPH program committee and is a regular reviewer for that conference. He has received best paper awards from the International Conference on Computer Vision and the European Conference on Computer Vision. He received an IEEE technical achievement award for 2005 for his research and was named an IEEE fellow in 2009. His recent textbook, Computer Vision: A Modern Approach (coauthored with J. Ponce and published by Prentice Hall) is now widely adopted as a course text.
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