Homer H. Chen
Department of Electrical Engineering, National Taiwan University
Homer H. Chen received the Ph.D. degree in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign.
Dr. Chen's professional career has spanned industry and academia. Since August 2003, he has been with the College of Electrical Engineering and Computer Science, National Taiwan University, where he is Distinguished Professor. Prior to that, he held various R&D management and engineering positions with U.S. companies over a period of 17 years, including AT&T Bell Labs, Rockwell Science Center, iVast, and Digital Island (acquired by Cable & Wireless). He was a U.S. delegate for ISO and ITU standards committees and contributed to the development of many new interactive multimedia technologies that are now part of the MPEG-4 and JPEG-2000 standards. His current research is related to multimedia signal processing, computational photography and display, and music data mining.
Dr. Chen is an IEEE Fellow. He currently serves on the IEEE Signal Processing Society Awards Committee and the Senior Editorial Board of the IEEE Journal on Selected Topics in Signal Processing. He was a General Co-chair of the 2019 IEEE International Conference on Image Processing. He served on the IEEE Signal Processing Society Fourier Award Committee and the Fellow Reference Committee from 2015 to 2017. He was a Distinguished Lecturer of the IEEE Circuits and Systems Society from 2012 to 2013. He was an Associate Editor of the IEEE Transactions on Circuits and Systems for Video Technology from 2004 to 2010, the IEEE Transactions on Image Processing from 1992 to 1994, and Pattern Recognition from 1989 to 1999. He served as a Guest Editor for the IEEE Transactions on Circuits and Systems for Video Technology in 1999, the IEEE Transactions on Multimedia in 2011, the IEEE Journal of Selected Topics in Signal Processing in 2014, and Springer Multimedia Tools and Applications in 2015.
It is important for any imaging device to accurately and quickly find the in-focus lens position so that sharp images can be captured without human intervention. In this overview talk, I will talk about the design criteria and considerations for both contrast detection autofocus (CDAF) and phase detection autofocus (PDAF) and highlight some key milestone techniques. In particular, I will close the talk by presenting how deep learning can be applied to push the performance of autofocus to an unprecedented level.