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Seminar: Stefano Soatto, UCLA

 July 23, 16:00, room A6 @ DIS

Stefano Soatto

I will discuss a notion of visual information as complexity not of the raw data, but of the images after the effects of nuisance factors such as viewpoint and illumination are discounted. It is rooted in ideas of J. J. Gibson, and stands in contrast to traditional information as entropy or coding length of the data regardless of its use, and regardless of the nuisance factors affecting it. Its computation is made possible by a recent characterization of the set of images modulo viewpoint and contrast changes, that induce group (invertible) transformations on the domain and range of the image. The non-invertibility of nuisances such as occlusion and quantization induces an "information gap" that can only be bridged by controlling the data acquisition process. Measuring visual information entails early vision operations, tailored to the structure of the nuisances so as to be "lossless" with respect to visual decision and control tasks (as opposed to data transmission and storage tasks implicit in traditional information theory). I illustrate these ideas on visual exploration, whereby a "Shannonian Explorer" navigates unaware of the structure of the physical space surrounding it, while a "Gibsonian Explorer" is guided by the topology of the environment, despite measuring only images of it, without performing 3D reconstruction. This operational definition of visual information suggests desirable properties that a visual representation should possess to best accomplish vision-based decision and control tasks.


Stefano Soatto received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 1996; he joined UCLA in 2000 after being Assistant and then Associate Professor of Electrical and Biomedical Engineering at Washington University, and Research Associate in Applied Sciences at Harvard University. Between 1995 and 1998 he was also Ricercatore in the Department of Mathematics and Computer Science at the University of Udine - Italy. He received his D.Ing. degree (highest honors) from the University of Padova- Italy in 1992. His general research interests are in Computer Vision and Nonlinear Estimation and Control Theory. In particular, he is interested in ways for computers to use sensory information (e.g. vision, sound, touch) to interact with humans and the environment. Dr. Soatto is the recipient of the David Marr Prize for work on Euclidean reconstruction and reprojection up to subgroups (with Y. Ma, J. Kosecka and S. Sastry). He also received the Siemens Prize with the Outstanding Paper Award from the IEEE Computer Society for his work on optimal structure from motion (with R. Brockett). He received the National Science Foundation Career Award and the Okawa Foundation Grant. He is a Member of the Editorial Board of the International Journal of Computer Vision (IJCV) and Foundations and Trends in Computer Graphics and Vision. He is the founder and director of the UCLA Vision Lab; more information is available at http://vision.ucla.edu.

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