| John Oliensis | |  |
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CPE 558:Computer Vision
An introduction to the field of Computer Vision, focusing on the underlying algorithmic, geometric, and optic issues. The course starts with a brief overview of basic image processing topics (convolution, smoothing, and edge detection). It then proceeds on various image analysis topics: binary images, moments-based shape analysis, Hough transform, image formation, depth and shape recovery, photometry, motion, classification, and special topics. |
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CS 558:Computer Vision
An introduction to the field of Computer Vision, focusing on the underlying algorithmic, geometric, and optic issues. The course starts with a brief overview of basic image processing topics (convolution, smoothing, and edge detection). It then proceeds on various image analysis topics: binary images, moments-based shape analysis, Hough transform, image formation, depth and shape recovery, photometry, motion, classification, and special topics. |
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CS 541:Artificial Intelligence
An introduction to the large and diverse field of artificial intelligence. Topics include: problem-solving by search and constraint satisfaction; alpha-beta search for two-player games; and logic and knowledge representation, planning, learning, decision theory, statistical learning, and computer vision. |
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CS 559:Machine Learning: Fundamentals and Applications
In many fields (e.g., computer vision, speech recognition, data mining, and bioinformatics), machine learning has become a crucial ingredient in translating research into applications. The course is intended to provide an in-depth overview of recent advances in machine learning, with applications in fields such as computer vision, data mining, natural language processing. Fundamental topics that will be covered include supervised (Bayesian) and unsupervised learning, non-parametric methods, graphical models (Bayes Nets and Markov Random Fields) and dimensionality reduction. The course will also cover several of the most important recent developments in learning algorithms, including boosting, Support Vector Machines and kernel methods, and outline the fundamental concepts behind these approaches.
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| | School: Schaefer School of Engineering & Science | | Department: Computer Science |
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| | Research & Education | |
| | Research | John Oliensis received his PhD in theoretical particles physics from the University of Chicago and carried out research in physics at Princeton University, the Fermi National Accelerator Laboratory, and the Argonne National Laboratory. He began research in Computer Vision in 1988, joining the University of Massachusetts at Amherst as a member of the research faculty. From 1994--2003 he was a research scientist at the NEC Research Institute, where he organized three workshops bringing together researchers in computer vision, human vision, neuroscience, and learning. Since 2003 he has been an associate professor in the computer science department at Stevens Institute of Technology. His interests include the estimation of object shape from images, perceptual organization, the recognition of objects, and human vision. He is a senior member of the IEEE, an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the author of over 60 papers.
| | Education | | BS Yale University, PhD University of Chicago 1981 |
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| | Experience & Service | |
| | Professional Service | | Associate Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence. Lead organizer for the 2007 IEEE International Workshop on "Beyond Multiview Geometry: Robust Estimation and Organization of Shapes from Multiple Cues," which was held in conjunction with the IEEE International Conference on Computer Vision and Pattern Recognition.
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| | Achievements & Professional Societies | |
| | Honors & Awards | | Davis Memorial Award for Research Excellence (Stevens), 2006 |
| | | Patents & Inventions | | Method for recovering 3D scene structure and camera motion directly from image intensities, awarded 2006 | | Professional Societies | | IEEE, Senior Member |
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