Edwin Hancock

Prof. Edwin R. Hancock studied physics as an undergraduate at the University of Durham and graduated with honors in 1977. He remained at Durham to complete the PhD degree in the area of high-energy physics in 1981. Following this, he worked for 10 years as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). In 1991, he moved to the University of York as a lecturer in the Department of Computer Science. He was promoted to senior lecturer in 1997 and to reader in 1998. In 1998, he was appointed to a chair in computer vision. Professor Hancock now leads a group of some 15 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical pattern recognition. He is currently working on graph-matching, shape-from-X, image databases, and statistical learning theory. He has published more than 90 journal papers and 350 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. In 1998, he became a fellow of the International Association for Pattern Recognition. He has been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition. He has also been a guest editor for special editions of the journals Image and Vision Computing and Pattern Recognition.

Publications

Graph-Based Methods in Computer Vision: Developments and Applications
Graph-Based Methods in Computer Vision: Developments and Applications
Xiao Bai, Jian Cheng, Edwin Hancock. © 2013. 395 pages.
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...
Graph Heat Kernel Based Image Smoothing
Graph Heat Kernel Based Image Smoothing
Zhang Fan, Edwin Hancock, Liu Shang. © 2013. 29 pages.
This chapter presents a new method for smoothing both gray-scale and color images, which relies on the heat diffusion equation on a graph. The image pixel lattice using a...