Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 120-137). Also available in electronic version.

Thesis (MScIng)--University of Stellenbosch, 2005. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--Oregon Graduate Center, 1986.

Thesis (Ph. D.)--Ohio State University, 1986. / Includes bibliographical references (leaves 166-170). Available online via OhioLINK's ETD Center

No description available.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 84-92).

Hand-printed Chinese character recognition presents an interesting problem for Artificial Intelligence research. Input data in the form of arrays of pixel values cannot be directly mapped to unique character identifications because of the complexity of the characters. Thus, intermediate data structures are necessary, which in turn lead to a need to represent knowledge of the characters' composition. Building the intermediate constructs for these hand-printed characters necessarily involves choices among ambiguities, the set of which is so large that an efficient search algorithm becomes central to the recognition process. Schema labelling is a theory of how knowledge should be organized for recognition tasks in which composition structure is inherent in the domain, the composition entails ambiguity, and the ambiguity generates large search spaces. This thesis describes an implementation of an enhanced version of schema labelling for Chinese characters. The specific problems addressed by the enhancements, with some success, are (i) the segmentation of real images into objects usable by the schema system, (ii) the definition of schemas which adequately describe the generic composition of hand-printed Chinese characters, as well as common variations or vagaries, and (iii) the inclusion of sufficient "control knowledge" to prevent combinatorial explosion of the backtracking recognition process. Test characters for recognition systems can be classified along several dimensions. On the spectrum from type-set, through hand-printed, to hand-written forms, our system was tested on restricted hand-print, at a level somewhat more difficult than is normally attempted. On the spectrum of input types, from grey-scale pixel input through on-line stroke representations, our system was fully tested only at the high end, with complete synthetic strokes. We obtained a success rate of 57%, 12 out of the 21 characters tested. The principal success of the work is that characters of the complexity tested could be recognized at all, and in the impact schema labelling techniques had on that recognition. / Science, Faculty of / Computer Science, Department of / Graduate

An investigation into the feasibility of applying pattern recognition concepts to the classification of metallic objects by their electromagnetic response was performed. The effect on the response of various factors such as object shape and orientation was examined and a pattern recognition scheme was proposed based on these results. Implementation of the proposal involved the development of a novel extension to the nearest mean vector type of classifier in which the class "centroid" was generalized to be a curve in the feature space rather than a point. The resultant pattern recognition scheme was tested on a representative test set which included 815 signatures of objects, corresponding to 104 variations in object and orientation. A success rate of greater than 98 percent was achieved. It is noted that the classifier extension developed provides a viable approach to classification of response signatures that vary continuously with respect to any single parameter. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

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