Grid-Based Genetic Algorithm Approach to Colour Image Segmentation
Image segmentation is a complex problem that has received lots of research in the past. Although there are many segmentation techniques, they all have their weaknesses. There is no single most effective technique that works best for every scenario.
Colour images increase the amount of information that needs to be processed, hence increasing the uncertainty. With such high levels of uncertainty and a problem that has no well-defined optimal solution, genetic algorithms have been used to a limited extent in segmenting images.
Segmentation is a computationally expensive task and the use of genetic algorithms to solve the problem requires large amounts of processing power. One way of obtaining this processing power is to make use of Grid computing. While genetic algorithm models have been developed and proven very successful for parallel architectures, almost none of this knowledge has been applied in developing a model for the Grid.
This research has the following major goals:
|© Marco Gallotta and Keri Woods 2007|