Visual sensing and processing have experienced a significant growth in the past several decades. Complementing the technology R&D dealing with visible signals and patterns are those exploiting a variety of nearly invisible “micro-signals,” which are typically an order of magnitude lower in strength or scale than the dominant ones. These micro-signals are ubiquitous but traditionally removed or ignored as nuances. Increasingly, it has been found that many of these micro-signals help us connect the physical world with the digital and cyber space, and harnessing them may bring about beneficial information that would otherwise harder to obtain. This talk will provide an overview on visualizing and analyzing several representative types of micro-signals, discussing the recent research advances and novel applications ranging from security to digital humanity to fitness and health.
Min Wu is a Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland, College Park. She received her Ph.D. degree in electrical engineering from Princeton University in 2001. At UMD, she leads the Media and Security Team (MAST), with main research interests on information security and forensics and multimedia signal processing. Her research and education have been recognized by a U.S. NSF CAREER award, a TR100 Young Innovator Award from the MIT Technology Review, an U.S. ONR Young Investigator Award, a Computer World "40 Under 40" IT Innovator Award, University of Maryland Invention of the Year Awards, IEEE Distinguished Lecturer recognition, and several paper awards from IEEE SPS, ACM, and EURASIP. She was elected IEEE Fellow and AAAS Fellow for contributions to multimedia security and forensics. Dr. Wu chaired the IEEE Technical Committee on Information Forensics and Security (2012-2013), and has served as Vice President - Finance of the IEEE Signal Processing Society (2010-2012), Founding Chief Editor of the IEEE SigPort initiative (2013-2014), and Editor-in-Chief of the IEEE Signal Processing Magazine (2015-2017).
The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in many applications such as image classification, detection and processing. Yet, CNN’s working principle remains a mystery. In this talk, I will first explain the operations of a computational neuron in detail using a signal processing approach. To go further, I will present new CNN-inspired signal transforms whose kernels (i.e., filter weights in the convolutional layers of CNNs) are derived from the second-order statistics of input image pixels in a one-pass feedforward manner. Neither data labels nor backpropagation computations are needed. Finally, the CNN and the CNN-inspired transform methodologies will be compared using common image processing examples.
Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of media processing, compression and understanding. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 140 students to their Ph.D. degrees and supervised 27 postdoctoral research fellows. Dr. Kuo is a co-author of 260 journal papers, 900 conference papers and 14 books.