Photocopy of typescript. / Digitized by Kansas Correctional Industries

A major problem in robotics vision is the segmentation of images of natural scenes in order to understand their content. This thesis presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of an image is formulated into production rules. A number of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include meta-rules that embody inferences about the order in which the knowledge rules are matched. They also include focus of attention rules that determine the path of processing within the image. A third set of rules contains the strategy rules which are data-driven inferences about the control rules. They dynamically modify the processing strategy. Different rule ordering and focus of attention strategies are selected according to a set of performance parameters. These measure the quality of the segmentation output at any point in time. Experiments with the knowledge rules resulted in an optimal set based on output quality and processing efficiency. Overall system performance is shown to be qualitatively and quantitatively superior to previous segmentation algorithms.

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Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references. Also available in electronic version.

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M. S.

The effects of manipulating discrete element failure types and font types were determined on a reading performance task using a plasma panel display. Thirty male and thirty female college students attending Virginia Polytechnic Institute and State University participated as subjects. The results demonstrate that reading performance is significantly degraded by the random addition or removal of discrete elements or lines of elements. Subjects took longer to read and made more null responses with lower case characters than with upper case characters. Similarly, reading performance was poorer in the discrete element failure condition than in the horizontal line or vertical line failure conditions. The Huddleston font was found to be better than the Lincoln/Mitre and the font used on the HP2621A. / M.S.

M.Com. (Informatics) / Please refer to full text to view abstract

D.Ing. (Electrical and Electronic ) / This thesis describes a neural network based system for the classification of handwritten digits as found on real-life mail pieces. The proposed neural network uses a modular architecture which lends itself to parallel implementation. This modular architecture is shown to produce adequate performance levels while significantly reducing the required training time. The aim of the system is not only to achieve a high recognition performance, but also to gain more insight into the functioning of the neural networks. This is achieved by using separate feature extraction and classification stages. The output of the feature extraction stage gives a good indication of the final performance level of the classifier, even before training. The need for an optimal feature set is expressed to elevate the performance levels even further.