Hilton San Francisco, Union Square
San Francisco, California, United States
2 - 6 February 2014
Plenary Events
Plenary Session and Society Award Presentations
Date: Tuesday 4 February 2014
Time: 8:30 AM - 9:50 AM
Location: Continental Ballroom 5
Using fMRI to Reverse Engineer the Human Visual System

Jack L. Gallant, Univ. of California, Berkeley (United States)

Abstract: The human brain is the most sophisticated image processing system known, capable of impressive feats of recognition and discrimination under challenging natural conditions. Reverse-engineering the brain might enable us to design artificial systems with the same capabilities. My laboratory uses a data-driven system identification approach to tackle this reverse-engineering problem. Our approach consists of four broad stages. First, we use functional MRI to measure brain activity while people watch movies. We divide these data into two parts, one used to fit models and one for testing model predictions. Second, we use a system identification framework based on multiple linearizing feature spaces to model activity measured at each point in the brain. Third, we inspect the most accurate models to understand how the brain represents structural and semantic information in the movies. Finally, we use the estimated models to decode brain activity, reconstructing the structural and semantic content in the movies. This framework could form the basis of practical new brain reading technologies, and can inform development of biologically-inspired computer vision systems.

Biography: Jack Gallant is Professor of Psychology at the University of California at Berkeley, and is affiliated with the graduate programs in Bioengineering, Biophysics, Neuroscience and Vision Science. He received his PhD. from Yale University and did post-doctoral work at the California Institute of Technology and Washington University Medical School. His research program focuses on computational modeling of human brain activity. These models accurately describe how the brain encodes information during complex, naturalistic tasks, and they show how information about the external and internal world are mapped systematically across the cortical surface. These models can also be used to decode information in the brain in order to reconstruct mental experiences.
Plenary Session and Conference Award Presentations
Date: Wednesday 5 February 2014
Time: 8:30 AM - 9:50 AM
Location: Continental Ballroom 5
Integrated Imaging: Creating Images from the Tight Integration of Algorithms, Computation, and Sensors

Charles Bouman, Purdue Univ. (United States)

Abstract: Some people suggest that imaging is a mature field, but nothing could be further from the truth. In fact, we are entering into the next phase of innovation in which a convergence of technologies is enabling the creation of an endless variety of imaging systems based on the tight integration of algorithms, computation, and sensor design. This new field, which we call integrated imaging, is evolving out of classical imaging modalities into a wide array of new applications. Integrated imaging systems will drive both scientific exploration and consumer products by blending novel and often counter-intuitive sensor design with algorithms that exploit the availability of enormous quantities of data and computation. This talk presents some examples of state-of-the-art integrated imaging systems based on computed tomography (CT), transmission electron microscopy (STEM), synchrotron beam imaging, optical sensing, and scanning electron microscopy (SEM). For each of these examples, we also explore their use and potential impact in applications ranging from healthcare to jet engine design. We conclude with some speculation on where integrated imaging might be going; where it might have greatest impact; and what will be the greatest challenges ahead.

Biography: Charles Bouman received a B.S.E.E. degree from the University of Pennsylvania in 1981 and a MS degree from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a Ph.D. in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Michael J. and Katherine R. Birck Professor of Electrical and Computer Engineering. He also holds a courtesy appointment in the School of Biomedical Engineering and is co-director of Purdue’s Magnetic Resonance Imaging Facility located in Purdue’s Research Park. Professor Bouman's research focuses on the use of statistical image models, multiscale techniques, and fast algorithms in applications including tomographic reconstruction, medical imaging, and document rendering and acquisition. Professor Bouman is a Fellow of the IEEE, a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), a Fellow of the society for Imaging Science and Technology (IS&T), a Fellow of the SPIE professional society. He is also a recipient of IS&T’s Raymond C. Bowman Award for outstanding contributions to digital imaging education and research, has been a Purdue University Faculty Scholar, and received the College of Engineering Engagement/Service Award, and Team Award. He was previously the Editor-in-Chief for the IEEE Transactions on Image Processing and a Distinguished Lecturer for the IEEE Signal Processing Society, and he is currently the Vice President of Technical Activities for IEEE Signal Processing Society. He has been an associate editor for the IEEE Transactions on Image Processing and the IEEE Transactions on Pattern Analysis and Machine Intelligence. He has also been Co-Chair of the 2006 SPIE/IS&T Symposium on Electronic Imaging, Co-Chair of the SPIE/IS&T conferences on Visual Communications and Image Processing 2000 (VCIP), a Vice President of Publications and a member of the Board of Directors for the IS&T Society, and he is the founder and Co-Chair of the SPIE/IS&T conference on Computational Imaging.
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