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Joseph MundyProfessor (Reaserch) Engineering |
Research Interests
Joseph Mundy is developing a Computer tomography (CT) analysis system for characterizing microscopic blood vessel structure in living tissue that will be applied to the rapid drug development of cancer blocking medicines. Another research thrust is automated image and video analysis for aerial reconnaissance. This research is aimed at scene description and change detection for military applications. He is also working on CMOS circuit techniques for probablistic computing at the nanoscale
Biography
Since 1963 Joe Mundy has been a technical staff member of the General Electric Research and Development Center. In 1969 he received his PhD in Electrical Engineering from Rensellaer Polytechnic Institute. His early projects at the Center include: high power microwave tube design, a super-conductive computer memory system, the design of high density associative memory arrays, the application of transform coding to image data compression and the development of an automatic inspection system for lamp filaments. From 1977 until 1982 he was Manager, Visual Information Processing Program. In this capacity he led a number of extensive efforts in the application of machine vision techniques to automatic visual inspection for quality control. In the 1980's Dr. Mundy formed a group to apply Image Understanding algorithms to aerial reconnaissance. He developed a system for aircraft recognition based on a sparse feature, called the vertex-pair, which achieved 98% recognition accuracy in a test on realistic airfield scenes. In the early 1990's, he was co-developer of a new approach to object representation and recognition based on geometric invariance. Over the last five years, he has participated in the RADIUS project, providing algorithms to the RADIUS Testbed System (RTS) which make use of the context provided by a 3D site model. These algorithms include change detection based on various levels of image segmentation and specific object structure matching. Dr. Mundy's professional activities involve active participation in the areas of pattern recognition and robotics. He was co-chairman of the workshop on industrial applications of machine vision which resulted in a special issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) in 1982. He is currently on the editorial board of the International Journal on Computer Vision. He also serves on the NSF advisory board for Artificial Intelligence and Robotics (IRIS). In 1988 he was named a Coolidge Fellow, the highest technical award in GE. He completed a year sabbatical as a visiting fellow at Oxford University during 1988-89. In 1993 Dr. Mundy was co-recipient of the Marr Prize, the leading prize in the field of Computer Vision. He is chairman of DARPA's Image Understanding Environment (IUE) Committee, which has specified and is supervising the development of the IUE. For the last 3 years he has chaired the RADIUS Image Understanding Advisory Committee which has prioritized the potential IU technology sources for RADIUS.
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Benjamin KimiaProfessor of Engineering |
Research Interests
Professor Kimia's research interests are in the areas of computer vision, image processing, medical imaging, perception, and psychophysics. A focus of his program is the problem of object recognition from shape. Free-form shape is represented by the geometry of its skeletal structure and the dynamics of flow of singularities along it-together referred to as the shock set. A natural grouping of shocks gives rise to a hierarchy represented as a graph, thus translating the recognition problem into graph matching. Notions of edit-distance in the shape space, categorization of shapes into classes, and their representation by a prototype or exemplar play a key role in successful indexing by shape into large image databases. Medical imaging is another focus of this research program. Three-dimensional datasets from computerized tomography (CT), magnetic resonance (MR) and other modalities are analyzed to extract relevant medical structure (segmentation), to register images over different scans, different modalities, and different patients (registration), for treatment follow-up, to create computational atlases, etc. The visualization and measurement of these structures can provide key clinical information to the physician. The generalization of the 2D shape representation to 3D is significant in applications requiring a notion of surfaces and volumes. We have classified the local forms of the 3D shock set, and the central skeletal sheets representing object symmetry. The mathematical classification of the transitions of the shock set order deformation is now established [Giblin-Kimia] in 2D but is open in 3D. Shape is completely reconstructable from its shock set, leading to a growth model of shape. We have built a language and a framework for creating and editing 2D free form and are now exploring its extension to the design of 3D form objects. Digital color halftoning is the problem of arranging colored dots, as produced by a modern printer (laser, inkjet, thermal, etc.) such that the perceptual effect is that of a continuous tone image, e.g., a photograph. This project involves the use of mathematical techniques in image processing, an understanding of the industrial use of color, and an examination of the perception of halftones.
Biography
Benjamin B. Kimia is a professor in the Division of Engineering at Brown University. He is also the associate director of the Laboratory for Engineering Man/Machine Systems (LEMS), an interdisciplinary group focused on signal and image processing, control, multimedia, and computer engineering. Dr. Kimia received the B.Eng. Honors degree from McGill University, Montreal, Canada in 1982, followed by M. Eng. (1986) and Ph.D. (1991) degrees in the areas of Computer Vision and Image Processing. Prof. Kimia's current research interests are focused on mathematical, psychophysical, neurophysiological, and computational models for visual processing with applications to medical imaging, indexing into large image databases using shape, and digital archaeology. His research program is based on symmetry-based representations of 2D and 3D images for segmentation, recognition, categorization, registration, and visualization.
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David B. CooperProfessor of Engineering |
Research Interests
Professor Cooper's current research focuses on the development and application of new geometric, algebraic, and probabilistic approaches, models, and algorithms for recognizing and estimating 2D and 3D geometric information from images, video, and range data. Present specific research projects include: Geometry-based searching of very large image databases (100,000 or more images) using new geometric invariants for open or closed curves as search features.
Biography
David B. Cooper received the BSc and ScM degrees in electrical engineering from the Massachusetts Institute of Technology (MIT) under the industrial co-op program in 1957 and the PhD degree in applied mathematics from Columbia University, New York, in 1966. From 1957 to 1966, he worked for Sylvania Electric Products, Inc., Mountain View, California and then for the Raytheon Co., Waltham, Massachusetts, on communications and radar systems analysis. Since 1966, he has been a professor of engineering at Brown University, Providence, Rhode Island. He was cofounder and associate director of the Brown University Laboratory for Engineering Man/Machine Systems (LEMS) from 1982 through 1997 and served on the executive committee of the Division of Engineering, Brown University for a few years. He was named a fellow of the IEEE for his research contributions, among the earliest papers, on unsupervised learning, combined supervised and unsupervised learning, and the Bayesian approach to image boundary estimation, segmentation, 3D stereo reconstruction, and optimally combining unreliable pieces of information in 3D. His more recent research interests have been on 2D and 3D object estimation and recognition from many unreliable fragments of information obtained from video or LIDAR, geometric learning, ad hoc sensor networks—specifically the theory for how to use 1,000 Cameras, and various data analysis problems for extraction of information from fragments found at archaeological sites.
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Gabriel TaubinAssociate Professor |
Research Interests
Prof. Taubin's main research interests fall within the following disciplines: Applied Computational Geometry, Computer Graphics, Geometric Modeling, 3D Photography, and Computer Vision. Since his graduate student days his research has been related to the development of efficient, simple, and mathematically sound algorithms to capture and operate on 3D obj