Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based...

The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques ma...

We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signal-to-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method was found to be fairly robust against noise. Clustering tests of four known protein structures we...