Modeling Images for Low-Level Vision and Image Enhancement

Marshall Tappen
University of Central Florida

Abstract:
Low-level vision and image enhancement problems, such as edge detection, denoising, and super-resolution, have been active areas of research since the beginnings of computer vision. We continue to make progress on these problems as we are able to create more accurate, powerful models of images. In this talk, I will review several popular methodologies for solving low-level vision problems, then discuss the directions in which models must evolve if we are to continue making progress on these problems.

Bio:
Marshall Tappen is an Assistant Professor of Computer Science at the University of Central Florida. He received a BS from Brigham Young University and a PhD from the Massachusetts Institute of Technology. His research focuses on computer vision, image enhancement, and machine learning.

Intelligent Decision Support for Intelligence Data Analysis

Qiang Shen
The Head of the Department of Computer Science
Aberystwyth University, UK

Abstract:
Decision support is a major application area of intelligent computing and intelligent systems. For instance, an intelligent system which is capable of automated modelling and analysis of intelligence data is of great significance to the detection and prevention of serious crime (e.g. terrorist activity). It is common knowledge that failures in detecting serious crime are not necessarily due to insufficient data, but rather to the difficulties in interpreting the available intelligence. Intelligent decision support systems can help in this regard, providing intelligence analysts with effective means in the assessment of possible crime scenarios. Such systems will also facilitate rapid response to the need of devising and deploying preventive measures through the computation of plausible emerging scenarios. This talk will introduce the important challenges which intelligent decision support faces, with a focus on applications to intelligence data monitoring. It will present a number of recent advances in the development of computational intelligence techniques in general, and fuzzy systems technology in particular. These advances contribute to the accomplishment of tasks essential for building intelligent decision support systems, not only for intelligence data but also for many other types of data (e.g. business and medical). The talk will conclude by identifying potential further research in the relevant field.

Bio:
Qiang Shen holds the established Chair in Computer Science and is the Head of the Department of Computer Science at Aberystwyth University, UK. He is also an Honorary Fellow at the University of Edinburgh, UK. Prof. Shen's research interests include: computational intelligence, fuzzy and qualitative modelling, reasoning under uncertainty, pattern recognition, data mining, and real-world applications of such techniques for decision support (e.g. crime detection, consumer profiling, systems monitoring, and medical diagnosis). He has a total of 28 years of working experience in these areas. Prof. Shen has been a long-serving associate editor of two premier IEEE Transactions (Systems, Man and Cybernetics, and Fuzzy Systems), and an editorial board member of several other leading international journals. He has chaired and given keynote lectures at many international conferences. Prof. Shen has authored 2 research monographs, and over 250 peer-reviewed papers. He has received several prestigious international awards, including an Outstanding Transactions Paper Award from IEEE. Prof. Shen has successfully supervised over 30 PDRAs and PhDs, including a British Computer Society Distinguished Dissertation Award winner.