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The Center for Imaging ScienceResearchers at the Center for Imaging Science (CIS) are developing systems that can interpret images of natural scenes, for example ordinary indoor and outdoor photographs, CT scans and other data obtained with bio-medical imaging devices, and aerial and satellite images acquired by remote sensing. Though great advances have been made in the acquisition of image data, e.g., the development of cameras and other imaging devices, and though the semantic understanding of the shapes and other objects appearing in images is effortless for human beings, the corresponding problem in machine perception, namely automatic interpretation via computer programs, remains a major open challenge in modern science. In fact, there are very few systems whose value derives from the analysis rather than production image data, and this "semantic gap" impedes scientific and technological advances in many areas, including automated medical diagnosis, industrial automation, and effective security and surveillance. The CIS, established in 1995 by the Army Research Office, brings together a diverse group of researchers whose work is highly interdisciplinary and rests on theoretical advances in mathematics and statistics, traditional signal and systems processing, and information theory. The principal faculty, Professors Donald Geman, John Goutsias, Michael Miller, Carey Priebe, Jerry Prince, and Assistant Professor Trac Tran, have appointments in the Departments of Biomedical Engineering, Electrical and Computer Engineering, and Mathematical Sciences. The research program at CIS is organized around three principal themes: 1) Representation and synthesis of complex shapes and scenes; 2) Computationally efficient shape detection and recognition; 3) Image formation and sensor modeling. To convey the flavor and purpose of the research at CIS consider the first theme. Due to the rapid development of imaging sensor technologies, investigators in the physical and biological sciences are now able to observe living systems and measure both their structural and functional behavior across many scales, from global, aggregate behavior to the microscopic scale of sub-cellular structure. Combining biomedical imaging science with computational modeling, we are now able to infer, non-invasively, the structural and functional properties of complex biological systems and neural circuits, for instance study the cohorts of neuropsychiatric illnesses including schizophrenia, depression, epilepsy, dementia of Alzheimer type, and Parkinson's. |
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