CS 495-01/595-10 Geospatial Vision and Visualization
Date Tuesday, March 29, 2011, 7:00 pm - 8:30 pm SB 111
Title: Sparsity: From Theory to Applications in Machine Learning/Vision and Medical Imaging
Speaker: Junzhou Huang
Today, sparsity techniques have been widely used to address practical problems in the fields of medical imaging, machine learning, computer vision, data mining and image/video analysis. This talk will briefly introduce the related sparsity techniques and their successful applications on compressive sensing, sparse learning, computer vision and medical imaging. Then, we will build a new framework called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. The new sparsity techniques under this framework have been successfully applied to different applications, such as compressive MR image reconstruction, video background subtraction, object tracking in visual surveillance, tag separation in tMRIs, computer-aided diagnosis and so on. The improved experimental results in these applications demonstrate the effectiveness of our new framework on large scale data.
Junzhou Huang is a graduating PhD student in the Department of Computer Science at Rutgers, The State University of New Jersey. He will be an Assistant Professor at University of Texas Arlington in the fall. His major research interests are focusing on medical imaging, machine learning and computer vision. He has published over 30 peer-reviewed articles in premier conferences and journals. He won the MICCAI Young Scientist Award 2010 in the society of medical imaging, and was selected as one of the 10 emerging leaders in multimedia and signal processing by the IBM T.J. Watson Research Center in 2010.