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

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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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published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Two basic models of human pattern recognition have been advanced: feature analysis and hypothesis testing. These can only be discriminated by looking at behaviour before recognition. This is studied here by having the subject scan with a pen that writes only where the (invisible) picture is black. Although a computer simulation shows that it is possible and efficient, subjects scanning capital letters and simple shapes with this technique rarely use hypothesis testing.

A computer program has been designed to interpret scenes from PEANUTS cartoons, viewing each scene as a two-dimensional representation of an event in the three-dimensional world. Characters are identified by name, their orientation and body position is described, and their relationship to other objects in the scene is indicated. This research is seen as an investigation of the problems in recognising flexible non-geometric objects which are subject to self-occlusion as well as occlusion by other objects. A hierarchy of models containing both shape and relational information has been developed to deal with the flexible cartoon bodies. Although the region is the basic unit used in the analysis, the hierarchy makes use of intermediate models to group individual regions into larger more meaningful functional units. These structures may be shared at a higher level in the hierarchy. Knowledge of model similarities may be applied to select alternative models and conserve some results of an incorrect model application. The various groupings account for differences among the characters or modifications in appearance due to changes in attitude. Context information plays a key role in the selection of models to deal with ambiguous shapes. By emphasising relationships between regions, the need for a precise description of shape is reduced. Occlusion interferes with the model-based analysis by obscuring the essential features required by the models. Both the perceived shape of the regions and the inter-relationships between them are altered. An heuristic based on the analysis of line junctions is used to confirm occlusion as the cause of the failure of a model-to-region match. This heuristic, an extension of the T-joint techniques of polyhedral domains, deals with "curved" junctions and can be applied to cases of multi-layered occlusion. The heuristic was found to be most effective in dealing with occlusion between separate objects; standard instances of self-occlusion were more effectively handled at the model level. This thesis describes the development of the program, structuring the discussion around three main problem areas: models, occlusion, and the control aspects of the system. Relevant portions of the programs analyses are used to illustrate each problem area.

Dissertation submitted in compliance with the requirements for Maters Degree in Technology: Mechanical Engineering, Technikon Natal, 2001. / This study presents a development of a method for recognition of a class of patterns in signals contaminated by strong noise. The class of signals considered is described by a finite alphabet. The target class of patterns is assumed to have specific statistical properties that can be conveniently captured by the position weight matrices (PWM) description. Itis also assumed thatthe 'signals: contain numerous patterns si~ilar to the patterns of the target class, but which belong to different classes. These other patterns represent the noise in the signals. The method for-improved recogrrition of the target class of patterns is based on clustering of the target motifs with regard to distance form the reference point (event) in the signal. This positional clustering enables more precise description of the target class of patterns by means of the PWMs. However, it requires the use of as many PWMs as there are clusters of the target class. The method developed is of general nature, applicable to the situations described. It is however, applied to the recognition of the specific short motifs in DNA sequences. The short motif considered is the TATA-box,one of the most important docking sites for proteins in Eukaryotic polymerase II promoter regions. The reference point in the singals obtained form DNA sequences the transcription .start site (TSS). Thus the positional dustering of the TATA-box motif resulted in 20 different PWMs, instead of only one that describes the whole TATA motif class. This however, resulted in more discriminative PWMs and the recognition accuracy has increased by about a factor of two when compared to the recognition of the TATA moti f based on the original PWM. / M