1. image and video modeling,
2. image and video segmentation,
3. object detection,
4. object and scene recognition,
5. high-level event and activity understanding,
6. motion estimation and tracking,
7. new inference and learning (both structure and parameters) theories for graphical models arising in vision applications,
8. generative and discriminative models, and
9. models incorporating contextual, domain, or common sense knowledge
• Q. Ji is with the Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180.
• J. Luo is with Kodak Research Laboratories, 1999 Lake Avenue, Rochester, NY 14650. E-mail: email@example.com.
• D. Metaxas is with the Department of Computer Science, Rutgers University, Piscataway, NJ 08854. E-mail: firstname.lastname@example.org.
• A. Torralba is with the Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.
• T.S. Huang is with the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801. E-mail: email@example.com.
• E.B. Sudderth is with the Department of Computer Science, 115 Waterman Street, Brown University, Box 1910, Providence, RI 02912.
For information on obtaining reprints of this article, please send e-mail to: firstname.lastname@example.org.
Qiang Ji received the PhD degree in electrical engineering from the University of Washington. He is currently a professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI). He is also a program director at the US National Science Foundation (NSF), managing part of NSF's computer vision and machine learning programs. He has held teaching and research positions with the Beckman Institute at the University of Illinois at Urbana-Champaign, the Robotics Institute at Carnegie Mellon University, the Department of Computer Science at the University of Nevada at Reno, and the US Air Force Research Laboratory. He currently serves as the director of the Intelligent Systems Laboratory (ISL) at RPI. His research interests are in computer vision, pattern recognition, and probabilistic graphical models. He has published more than 150 papers in peer-reviewed journals and conferences. His research has been supported by major governmental agencies including NSF, NIH, DARPA, ONR, ARO, and AFOSR, as well as by major companies including Honda and Boeing. He is an editor on several computer vision and pattern recognition related journals and he has served as a program committee member, area chair, and program chair for numerous international conferences/workshops. Professor Ji is a senior member of the IEEE.
Jiebo Luo received the PhD degree from the University of Rochester in 1995. He is a senior principal scientist with the Kodak Research Laboratories, Rochester, New York. His research interests include image processing, machine learning, computer vision, multimedia data mining, and computational photography. He has authored more than 150 technical papers and holds 50 US patents. Dr. Luo has been involved in organizing numerous leading technical conferences sponsored by the IEEE, ACM, and SPIE, most recently being the general chair of the 2008 ACM International Conference on Content-based Image and Video Retrieval (CIVR), area chair of the 2008 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), and program cochair of the 2007 SPIE International Symposium on Visual Communication and Image Processing (VCIP). He serves or has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI), the IEEE Transactions on Multimedia ( TMM), Pattern Recognition ( PR), and the Journal of Electronic Imaging. He has been a guest editor for a number of special issues, including "Real-World Image Annotation and Retrieval" ( TPAMI, 2008) and "Integration of Content and Context for Multimedia Management" ( TMM, 2009). He is a fellow of the IEEE and of SPIE.
Dimitris Metaxas received the PhD degree in 1992 from the University of Toronto and was a tenured faculty member at the University of Pennsylvania from 1992 to 2001. He is a Professor II (Distinguished) in the Computer Science Department at Rutgers University. He is currently directing the Center for Computational Biomedicine, Imaging, and Modeling (CBIM). Dr. Metaxas has been conducting research toward the development of formal methods upon which both computer vision, computer graphics, and medical imaging can advance synergistically, as well as on massive data analytics problems. In computer vision, he works on the simultaneous segmentation and fitting of complex objects, shape representation, deterministic and statistical object tracking, learning, ASL, and human activity recognition. In medical image analysis, he works on segmentation, registration, and classification methods for cardiac and cancer applications. In computer graphics, he is working on physics-based special effects methods for animation. He has pioneered the use of Navier-Stokes methods for fluid animations that were used in the movie Antz in 1998. He has published more than 300 research articles in these areas and has graduated 27 PhD students. His research has been funded by the NSF, NIH, ONR, AFOSR and ARO. He has published a book on his research activities titled Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging (Kluwer Academic). He is on the editorial board of Medical Imaging and is an associate editor of GMOD and CAD. Dr. Metaxas has received several best paper awards for his work on in the above areas. He was awarded a Fulbright Fellowship in 1986, is a recipient of NSF Research Initiation and Career awards, an ONR YIP, and is a fellow of the American Institute of Medical and Biological Engineers and a member of the ACM and of the IEEE. He was also the program chair of ICCV '07 and the general chair of MICCAI '08.
Antonio Torralba received the degree in telecommunications engineering from the Universidad Politécnica de Cataluña, Spain, and was awarded the PhD degree in signal, image, and speech processing by the Institut National Polytechnique de Grenoble, France. He is an associate professor of electrical engineering and computer science in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). He spent postdoctoral training at the Brain and Cognitive Science Department and the Computer Science and Artificial Intelligence Laboratory at MIT. He is a member of the IEEE.
Thomas S. Huang received the ScD degree from the Massachusetts Institute of Technology (MIT) in electrical engineering and was on the faculty of MIT and Purdue University. He joined the University of Illinois at Urbana-Champaign in 1980 and is currently the William L. Everitt Distinguished Professor of Electrical and Computer Engineering, Research Professor of the Coordinated Science Laboratory, Professor of the Center for Advanced Study, and cochair of the Human Computer Intelligent Interaction major research theme of the Beckman Institute for Advanced Science and Technology. Professor Huang is a member of the National Academy of Engineering and has received numerous honors and awards, including the IEEE Jack S. Kilby Signal Processing Medal and the King-Sun Fu Prize of the International Association of Pattern Recognition. He has published 21 books and more than 600 technical papers in network theory, digital holograpy, image and video compression, multimodal human computer interfaces, and multimedia databases. He is a fellow of the IEEE.
Erik B. Sudderth received the bachelor's degree (summa cum laude) in electrical engineering from the University of California, San Diego, and the master's and PhD degrees in electrical engineering and computer science from the Massachusetts Institute of Technology. He is an assistant professor in the Brown University Department of Computer Science. From 2006 through 2009, he was a postdoctoral scholar at the University of California, Berkeley. His research interests include probabilistic graphical models, nonparametric Bayesian methods, and applications of statistical machine learning in object recognition, tracking, visual scene analysis, and image processing. He was awarded a National Defense Science and Engineering Graduate Fellowship (1999), an Intel Foundation Doctoral Fellowship (2004), and in 2008 was named one of "AI's 10 to Watch" by IEEE Intelligent Systems. He is a member of the IEEE.