Talk by Prof. Hassan Foroosh
Adaptive Compressive Sensing by k-significant Sampling of Haar Coefficients
Compressive sensing is a new imaging paradigm that was introduced in the last few years as an alternative to classical sampling and information theory. The talk begins by introducing this new paradigm and its underlying principle. It will then provide a brief survey of the methods proposed in recent literature and discusses their pros and cons. Much of recent work in this area has focused on random sampling using incoherent sampling and reconstruction basis - the so called restricted isometry property. This talk will introduce the idea of coherent sampling and reconstruction of k-sparse signals; and shows that such adaptive sampling over an orthonormal basis (e.g. Haar wavelets) produces superior results.
Short Bio
Hassan Foroosh is an Associate Professor of Computer Science in the School of Electrical Engineering and Computer Science at the University of Central Florida (UCF) and is the director of the Computational Imaging Laboratory (CIL) at UCF. Prior to joining UCF, he was a senior research scientist at the University of California, Berkeley (2000-2002), and prior to that an assistant research scientist at Center for Automation Research and the Institute for Advanced Computer Studies of the, University of Maryland, College Park (1997-2000). He received the MS and PhD degrees in Computer Science, specializing in computer vision and Image Processing, from INRIA-Sophia Antipolis in France in 1993, and 1996, respectively.
He has authored and co-authored over 100 peer-reviewed journal and conference papers, and has been in the organizing and the technical committees of several international colloquia, such as ICCV, CVPR, ECCV, ICIP, and ICPR. Dr. Foroosh is a senior member of IEEE, and was an Associate Editor of the IEEE Transactions on Image Processing in 2003-2008. In 2004, he was a recipient of an academic excellence award from Sun MicroSystems, and the Pierro Zamperoni award from the International Association for Pattern Recognition (IAPR). He also received the Best Scientific Paper Award in the International Conference on Pattern Recognition of IAPR. His research has been sponsored by NASA, NSF, ONR, FPCE, and industry.
