September 2011 (VOL. 33, No. 9) pp. 1697-1698
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Published by the IEEE Computer Society
Published by the IEEE Computer Society
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We are happy to announce the appointment to the TPAMI Editorial Board of Tim Cootes, Mark Everingham, Jiaya Jia, Tomas Pajdla and Tinne Tuytelaars. They will be handling a broad range of papers, as usual, but their emphasis will be as follows: Professor Cootes, shape and appearance modeling; Professor Everingham, probabilistic understanding of images and videos; Professor Jia, vision and graphics; Professor Pajdla, geometry and algebra in computer vision; and Professor Tuytelaars, object recognition and local features..
Brief biographies of these distinguished additions to the editorial board appear below. Welcome aboard, and thank you in advance for all of your hard work!
Since Editorial Board members serve a limited term under IEEE policy, we also have to announce the departure of Jiebo Luo, Patros Maragos, Kevin Murphy, Nalini Ratha, and John Winn. Their services to the journal and the community are greatly appreciated by all of us.
Ramin Zabih, Editor-in-Chief
Zoubin Ghahramani, Associate Editor-in-Chief
Sing Bing Kang, Associate Editor-in-Chief
Jiri Matas, Associate Editor-in-Chief
Timothy F. Cootes recieved the BSc degree with honors in mathematics and physics from Exeter University, England, in 1986, and the PhD degree in engineering from Sheffield City Polytechnic in 1991. He is currently a professor of computer vision at the University of Manchester. His research interests include statistical models of shape and appearance variation and their applications in face image interpretation and medical image analysis. He is a fellow of the International Association for Pattern Recognition.
Mark Everingham completed the PhD degree in computer vision at the Department of Computer Science, University of Bristol, and received the BS degreec in cmputer science from the University of Manchester. He is currently an RCUK Academic Fellow in the Vision Group of the School of Computing at the University of Leeds. Previously he was a Research Fellow in the Visual Geometry Group at the University of Oxford, where he worked on the EU-funded CLASS and CogViSys projects with Andrew Zisserman. He was a Fulford Junior Research Fellow of Somerville College. His primary research interests are in high-level understanding of images and video, with an emphasis on probabilistic approaches derived by statistical learning from examples. One application of this work investigated during his PhD degree work was a mobility aid for the severely visually impaired. His work in this domain has led to applications of probabilistic approaches in other areas, such as object extraction from video sequences, and formal methods for evaluating complex vision techniques, such as image segmentation algorithms. More recently his research has focused on methods for recognizing objects, people and their pose and behavior in unconstrained consumer images and video. A central theme to this work has been the exploitation of “weak” but readily available sources of supervision, with examples including learning models for face recognition from video scripts and subtitles, learning to recognize object categories from textual descriptions alone, learning sign language by “watching TV,” and learning articulated pose estimation from inaccurate Mechanical Turk annotation. He is a co-organizer of the PASCAL Visual Object Classes (VOC) challenge. He has served as an area chair for the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) and the European Conference on Computer Vision (ECCV), and regularly reviews for all of the main conferences and journals. In 2008, he served as general and program chair of the British Machine Vision Conference (BMVC2008). In 2009, he was elected to the executive committee of the British Machine Vision Association (BMVA).
Jiaya Jia received the PhD degree in computer science from the Hong Kong University of Science and Technology in 2004 and is currently an associate professor in the Department of Computer Science and Engineering at the Chinese University of Hong Kong (CUHK). He was a visiting scholar at Microsoft Research Asia from March 2004 to August 2005 and conducted collaborative research at Adobe Systems in 2007. He leads the research group at CUHK for computer vision and 2D computer graphics, focusing specifically on computational photography, 3D reconstruction, practical optimization, and motion estimation. He served as an area chair for ICCV 2011 and was on the program committee of several major conferences, including ICCV, ECCV, and CVPR. He cochaired the Workshop on Interactive Computer Vision in conjunction with ICCV 2007 and the ACM International Conference on Virtual Reality Continuum and Its Applications (VRCIA) 2006. He won the Young Researcher Award 2008 and Research Excellence Award 2009, conferred by the Chinese University of Hong Kong.
Tomas Pajdla received the MS and PhD degrees in electrical fngineering from the Czech Technical University (CTU), Prague. He has been an assistant professor at CTU since 1995. He is working in geometry and algebra of computer vision with applications in multiview geometry, nonclassical imaging, photogrammetry, and robotics. He coauthored works that introduced epipolar geometry of panoramic cameras, investigated the use of panoramic images for robot localization, contributed to studies of panoramic mosaics, and studied omni-directional cameras that do not have a center of projection but have a generalization of epipolar geometry. He has contributed to image matching, solving stereo correspondence problem, and to algebraic techniques in computer vision. He has published more than 50 scientific works in scientific journals and refereed conferences. In 1998, he was awarded the best paper Prize at the OAGM Conference in Austria; he was awarded the best scientific paper prize at the British Machine Vision Conference in 2002 and at the Austrian Conference on Image Processing and Pattern Recognition in 1998. In 2005, his students ranked second in the ICCV camera localization contest.
Tinne Tuytelaars received the Msc and PhD degrees in electrical engineering from the Katholieke Université Leuven (K.U.Leuven), Belgium, in 1996 and 2000 respectively. Since then, she has been affiliated with K.U.Leuven, first as a postdoctoral researcher and, since 2008, as a research professor, with short visits to INRIA (Grenoble), NICTA (Canberra) ,and ICSI/University of California, Berkeley. In 2009, she was awarded an ERC Starting Independent Researcher Grant. She is a member of the editorial board of Computer Vision and Image Understanding, program chair of the European Conference on Computer Vision 2014, and has recently been area chair for all major computer vision conferences. Her research focuses on computer vision and, in particular, image representations, object recognition, and multimodal analysis (combining images with text). She is best known as one of the pioneers of local invariant features, advocating their use for wide baseline matching as well as object recognition.
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