Description
Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data.
Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.
Author's/Editor's Biography
Jian Cheng (Ed.)
Jian Cheng is currently an associate professor of Institute of Automation, Chinese Academy of Sciences. He received the B.S. and M.S. degrees in Mathematics from Wuhan University in 1998 and in 2001, respectively. In 2004, he got his Ph.D degree in pattern recognition and intelligent systems from Institute of Automation, Chinese Academy of Sciences. From 2004 to 2006, he has been working as postdoctoral in Nokia Research Center. Then he joined National Laboratory of Pattern Recognition, Institute of Automation. His current research interests include image and video search, machine learning, etc. He has authored or co-authored more than 40 academic papers in these areas. He was awarded LU JIAXi Young Talent Prize in 2010. Dr. Cheng served as Technical Program Committee member for some international conferences, such as ACM Multimedia 2009 (content), IEEE Conference on Computer Vision and Pattern Recognition (CVPR’ 08), IEEE International Conference on Multimedia and Expo (ICME’ 08), Pacific-Rim Conference on Multimedia (PCM’ 08), IEEE International Conference on Computer Vision (ICCV’ 07), etc. He has also co-organized one special issue on Pattern Recognition Journal, and several special sessions on PCM 2008, ICME 2009, PCM 2010.