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The analysis of three dimensional scenes from image sequences has a number of goals. These include (but are not limited to): (i) the recovery of 3D scene structure, (ii) the detection of moving objects in the presence of camera induced motion, and (iii) the synthesis of new camera views based on a given set of views.
Previous approaches to the problem of dynamic scene analysis can be broadly divided into two classes: (i) 2D algorithms which apply when the scene can be approximated by a flat surface and/or when the camera is only undergoing rotations and zooms, and (ii) 3D algorithms which work well only when significant depth variations are present in the scene and the camera is translating.
This talk we will present a unified approach to dynamic scene analysis in both 2D and 3D scenes, with a strategy to gracefully bridge the gap between those two extremes. Our approach is based on a stratification of the problem into scenarios which gradually increase in their complexity. We present a set of techniques that match the above stratification. These techniques progressively increase in their complexity, ranging from 2D techniques to more complex 3D techniques. Moreover, the computations required for the solution to the problem at one complexity level become the initial processing step for the solution at the next complexity level. We illustrate these techniques using examples from real image sequences.
Michal Irani received her B.Sc. degree in Mathematics and Computer Science from the Hebrew University of Jerusalem, Israel, in 1985, and her M.Sc. and Ph.D. in Computer Science from the Hebrew University of Jerusalem in 1989 and 1994, respectively. Since 1993 she has been a member of the technical staff of the Vision Technologies Lab at avid Sarnoff Research Center (SRI), Princeton, NJ. Her current research are visual motion analysis, video representations, processing and analysis, video compression, and multi-sensor image fusion.
P. Anandan received his PhD in Computer Science from University of Massachussetts, Amherst, Ma., in 1987. From 1987-1991 he was an Assistant Professor of Computer Science at Yale University, New Haven, Ct. Since 1990 he has been at the David Sarnoff Research Center, where he is currently the Head of the Video Information Processing Research Group. His current research interests include early vision representations, visual motion analysis, video sequence representations, processing, and analysis, real-time vision processing, scalable video compression, and vision for navigation and surveillance.