Research Group of Visvanathan Ramesh

System Engineering for Computer Vision

Prof. Dr. Visvanathan Ramesh

Adjunct Fellow, Frankfurt Institute of Advanced Studies
W3 Professor, J.W. Goethe University, Frankfurt
Project Coordinator, BFNT-Frankfurt
Ruth-Moufang-Str. 1,
60438 Frankfurt am Main, Germany
Email: rameshfias.uni-frankfurtde
Phone: +49 69 798 47625
Fax: +49 69 798 47615


Over the last 20 years of my research career, I have focused on formal design, analysis and validation of computer vision systems. The central theme of the work emerged from my Ph.D. dissertation at the University of Washington that was supervised by my mentor Professor Robert Haralick.  This dissertation developed a systematic engineering methodology for performance characterization of vision systems. My dissertation focused on performance modeling and optimization of a system whose design was provided apriori.  In subsequent dissertations of Ph.D. students (that I co-advised under support from Siemens) we have focused on the development of contextual models (Bayesian Networks) for Video Monitoring, exploring how modularity in the domain models can be exploited to perform real-time inference, systems identification for a broader range of vision algorithms, and semi-automatic or automatic programming for vision. Our most recent work is focused on understanding relationships between our systems engineering work and recent advances in neuroscience, psychology, artificial intelligence and cognitive science.

I have collaborated with several colleagues to make fundamental advances in problems such as:  object detection & tracking, background modeling and change detection, segmentation, motion analysis, sequential and online estimation of temporal statistical models, non-parametric statistical methods and their applications in computer vision, photometric invariants etc. The joint work with my Ph.D students and colleagues have largely influenced the development of practical, high performance real-world systems for safety, security and industrial automation. 

This website provides links to published work that is the result of collaboration with my graduate students, past colleagues at Siemens Corporate Research and various university professors across the globe.

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