|Date:||Thursday, October 18, 2018|
I will give a brief tutorial on sparse representation of signals and images using over-complete dictionaries. I will then discuss deterministic and Bayesian sparsity-based methods for image restoration and denoising. Finally, I will present new results on unsupervised spectral unmixing of hyperspectral imaging data using our recently developed multi-channel least angle regression algorithm, a new sparsity-based method.
Bio: Dr. Sherif is an Associate Professor in the Electrical & Computer Engineering Department and is a core faculty member of the Graduate Biomedical Engineering Program at The University of Manitoba. His research interests include Digital Image Processing (M.Sc., University of Wisconsin-Madison) and Optics (Ph.D., University of Colorado at Boulder). Before arriving at the University of Manitoba, he held research positions at The University of Oxford, Imperial College London and National Research Council of Canada. He was also a Lecturer in Applied Optics (assistant professor) at the University of Kent, UK. He is author or co-author of over 100 scientific publications, including four patents.
December 10 – December 21: Fall Term Exam Period
December 22 – January 2: Winter Holiday (University Closed)
Statistics seminar: Erfan Houqe: “Random effects covariance matrix modeling for longitudinal data with covariates measurement error” — Thursday, January 17 at 2:45 p.m., P230 Duff Roblin.