Ragav Venkatesan is currently completing his Ph.D. study in Computer Science in the School of Computing, Informatics and Decision Systems Engineering at Arizona State University. He has been a Research Associate with the Visual Representation and Processing Group in ASU, and has worked as a Teaching Assistant for several graduate-level courses in machine learning, pattern recognition, video processing and computer vision. Prior to this, he was a Research Assistant with the Image Processing and Applications Lab in the School of Electrical & Computer Engineering at ASU, where he obtained an M.S. degree in 2012. From 2013 to 2014, Venkatesan was with the Intel Corporation as a computer vision research intern working on technologies for autonomous vehicles. Venkatesan regularly serves as a reviewer for several peer-reviewed journals and conferences in machine learning and computer vision.Baoxin Li received his Ph.D. in electrical engineering from the University of Maryland, College Park, in 2000. He is currently a Professor and Chair of the Computer Science and Engineering program, and a Graduate Faculty in Electrical Engineering and Computer Engineering programs at Arizona State University, Tempe. From 2000 to 2004, he was a Senior Researcher with SHARP Laboratories of America, Camas, Washington, where he was a technical lead in developing SHARP’s trademarked HiMPACT Sports technologies. From 2003–2004, he was also an Adjunct Professor with the Portland State University, Oregon. He holds eighteen issued U.S. patents and his current research interests include computer vision and pattern recognition, multimedia, social computing, machine learning, and assistive technologies. He won twice the SHARP Laboratories’ President Award, in 2001 and 2004 respectively. He also won the SHARP Laboratories’ Inventor of the Year Award in 2002. He was a recipient of the National Science Foundation’s CAREER Award.

Select Guide Rating

• Dedication; Acknowledgements; About the Author; Preface ; Chapter 1: Introduction to visual computing; Chapter 2: Learning as a regression problem; Chapter 3: Artificial neural networks; Chapter 4: Convolutional neural networks; Chapter 5: Modern and novel usages of CNNs; Appendix; Postscript