A Signal Processing Approach to Modeling Vision and Applications
Current state-of-the-art algorithms that process visual information for end use by humans treat images and video as traditional signals and employ sophisticated signal processing strategies to achieve their excellent performance. These algorithms also incorporate characteristics of the human visual system (HVS), but typically in a relatively simplistic manner, and achievable performance is reaching an asymptote. However, large gains are still realizable with current techniques by aggressively incorporating HVS characteristics, combined with a good dose of clever signal processing. Achieving these gains requires HVS characterizations which better model natural image perception ranging from sub-threshold perception (where distortions are not visible) to suprathreshold perception (where distortions are clearly visible). In this talk, I will present results from our lab characterizing the responses of the HVS to natural images, and contrast these results with ‘classical’ psychophysical results. I will also present applications of these results to image compression and quality assessment, as well as some signal processing problems (and their solutions) that emerged in applying the psychophysical results.
Dr. Sheila S Hemami
Professor of Electrical Engineering, Cornell University on April 27, 2007 at 12:00 PM in Engineering Building II, Room 1230
Sheila S. Hemami received the B.S. degree (summa cum laude) in EE from the University of Michigan, Ann Arbor, in 1990 and the M.S. and Ph.D. degrees in EE from Stanford University, Stanford, CA, in 1992 and 1994 respectively. She was a member of technical staff at Hewlett Packard Laboratories in Palo, Alto, California in 1994. Upon completing her Ph.D., she joined the School of Electrical Engineering at Cornell where she directs the Visual Communications Lab and is a professor and currently Associate Director of the School.
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