By Dr. Gerald Schaefer, Loughborough University, United Kingdom.
Metaheuristic algorithms such as genetic algorithms or particle swarm optimisation are algorithms that employ heuristics to tackle the issue of large solutions spaces in optimisation problems. They are problem-independent and thus can be used for a variety of applications, while population-based approaches also allow the sharing of information among different candidate solutions for further efficacy. In my talk, I will show how we successfully use recent population-based metaheuristics including the Self-Organizing Migration Algorithm (SOMA) and Human Mental Search (HMS) for image processing applications such as colour quantisation and multi-level thresholding.
Gerald Schaefer gained his PhD in Computer Vision from the University of East Anglia. He worked at the Colour & Imaging Institute, University of Derby (1997-1999), in the School of Information Systems, University of East Anglia (2000-2001), in the School of Computing and Informatics at Nottingham Trent University (2001-2006), and in the School of Engineering and Applied Science at Aston University (2006-2009) before joining the Department of Computer Science at Loughborough University where he leads the Vision, Imaging and Autonomous Systems Research Group.
His research interests are mainly in the areas of colour image analysis, image retrieval, physics-based vision, medical imaging, and computational intelligence. He has published extensively in these areas with a total publication count exceeding 300. He has been invited as keynote or tutorial speaker to numerous conferences, is the organiser of various international workshops and special sessions at conferences, and the editor of several books, conference proceedings and special journal issues. He is a member of the editorial board of more than 10 international journals, reviews for over 80 journals and served on the programme committee of more than 200 conferences.