Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
Henry Braun is a researcher at Magnetic Resonance Research center at University of Minnesota. He received his Ph.D. in electrical engineering from Arizona State University in 2016. His research interests include computer vision, signal processing, and compressive sensing.
Pavan Turaga (S'05, M'09, SM'14) is an associate professor in the School of Arts, Media, Engineering, and Electrical Engineering at Arizona State University. He received a B.Tech. degree in electronics and communication engineering from the Indian Institute of Technology Guwahati, India, in 2004, and the M.S. and Ph.D. in electrical engineering from the University of Maryland, College Park in 2008 and 2009, respectively. He then spent two years as a research associate at the Center for Automation Research, University of Maryland, College Park. His research interests are in imaging and sensor analytics with a theoretical focus on non-Euclidean and high-dimensional geometric and statistical techniques. He was awarded the Distinguished Dissertation Fellowship in 2009. He was selected to participate in the Emerging Leaders in Multimedia Workshop by IBM, New York, in 2008. He received the National Science Foundation CAREER award in 2015.
Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (also an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, machine learning, and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award-winning iPhone/iPad and Android versions. He is author of two textbooks: Audio Processing and Coding by Wiley and DSP: An Interactive Approach (2nd Ed.). He contributed to more than 300 papers, 7 monographs, 9 full patents, 6 provisional patents, and 10 patent pre-disclosures. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal processing society in 2004. He is a series editor for the Morgan and Claypool lecture series on algorithms and software. He received recently the 2018 IEEE Phoenix Chapter award with citation: ""For significant innovations and patents in signal processing for sensor systems."" He also received the 2018 IEEE Region 6 Educator Award (across 12 states) with citation: ""For outstanding research and education contributions in signal processing.""
Sameeksha Katoch is a Ph.D.student in the School of Electrical, Computer, and Energy Engineering at Arizona State University. She received a B.Tech. Degree in electronics and communication engineering from the National Institute of Technology Srinagar, India, in 2015, and a M.S. degree in electrical engineering from the Arizona State University in 2018. She has received IEEE Al Gross Student Award. Her research interests are in computer vision, signal processing, and deep learning.
Suren Jayasuriya has been an assistant professor at Arizona State University since 2018. He was a postdoctoral fellow at the Robotics Institute at Carnegie Mellon University, U.S. in 2017. He received a B.S. in mathematics and a B.A. in philosophy from the University of Pittsburgh, in 2012, and received his Ph.D. in Electrical and Computer Engineering from Cornell University, in 2017. His research interests are in computational photography and imaging, computer vision, and image sensors.
Cihan Tepedelenlioglu (S'97-M'01) was born in Ankara, Turkey in 1973. He received his B.S. with highest honors from Florida Institute of Technology in 1995, and his M.S. from the University of Virginia in 1998, both in Electrical Engineering. From January 1999 to May 2001 he was a research assistant at the University of Minnesota, where he completed his Ph.D. in Electrical and Computer Engineering. He is currently an Associate Professor of Electrical Engineering at Arizona State University. He was awarded the NSF (early) Career grant in 2001, and has served as an Associate Editor for several IEEE Transactions including IEEE Transactions on Communications, IEEE Signal Processing Letters, and IEEE Transactions on Vehicular Technology. His research interests include statistical signal processing, system identification, wireless communications, estimation and equalization algorithms for wireless systems, multi-antenna communications, OFDM, ultra-wideband systems, distributed detection and estimation, and data mining for PV systems.
