Workshop topics include (but are not limited to):
The LLNL Center for Advanced Signal and Image Sciences (CASIS) exists to establish a forum where research scientists and engineers can freely exchange information and ideas in a comfortable intellectual environment focused on the areas of the signal and image sciences.
Read more about the CASIS mission.
The workshop is open to all with an interest in the signal and image sciences, and there is no fee to attend.
Proper identification, such as a U.S. Government ID, driver license (for U.S. citizens) or passport (for foreign nationals), is required for entrance to the venue.
Past Workshops have hosted such luminaries as Richard Baraniuk (Compressive Sensing), James Flanagan (Speech Analysis), Alan Oppenheim (Signal Processing), Bernard Widrow (Adaptive Filters), and Ronald Bracewell (Fourier Imaging).
Over the last two decades, model-based imaging techniques have emerged as a principled framework for understanding and solving many of the most important problems in imaging research. The approach of model-based imaging is to construct a model of both the image and the imaging system, and then to use this integrated model to either reconstruct an unknown image, or to estimate unknown parameters. So for example, model-based image reconstruction and parameter estimation can be used to robustly form images from sensors with uncertain calibration. But in addition, model-based imaging can serve as a framework for optimizing the static and dynamic design of imaging sensor systems themselves.
In this talk, we review some techniques and recent successes in model-based imaging. Two application domains that we consider are tomographic reconstruction from multislice helical-scan CT and electron microscopy, two very different sensors that share much in common when viewed from the perspective of model-based imaging. For both cases, we discuss a variety of technical innovations, which either improve image quality or reduce the computational burden. We then show results, which demonstrate the value of the methods both quantitatively and qualitatively, on a variety of real and simulated datasets. Finally, we conclude with a philosophical discussion of the future potential of model-based methods, and we present some emerging ideas, which have the potential to change the field.
Charles A. Bouman is the Michael J. and Katherine R. Birck Professor of Electrical and Computer Engineering at Purdue University where he also holds an appointment in the School of Biomedical Engineering and serves has a co-director of Purdue's Magnetic Resonance Imaging Facility. He received his B.S.E.E. degree from the University of Pennsylvania, M.S. degree from the University of California at Berkeley, and Ph.D. from Princeton University in 1989.
Professor Bouman's research focuses on inverse problems, stochastic modeling, and their application in a wide variety of imaging problems including tomographic reconstruction and image processing and rendering. Prof. Bouman is a Fellow of the IEEE, AIMBE, IS&T, and SPIE. He has served as the Editor-in-Chief of the IEEE Transactions on Image Processing, Distinguished Lecturer for the IEEE Signal Processing Society, a member of the IEEE Signal Processing Society's Board of Governors, and the Vice President of Publications for the IS&T Society. Currently, he is Vice President for Technical Directions of the IEEE Signal Processing Society.
Further information about Professor Bouman's research activities is available at his Web site.
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