Thesis (Ph. D.)--University of Hong Kong, 2005.

Thesis (M.Comp. Sc.)--Dept. of Computer Science, Concordia University, 1995. / Includes bibliographical references (leaves 119-128) and index. Available also on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 96-109). Also available on the Internet.

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.

D. Ing. / This thesis describes a real world application in the field of pattern recognition. License plate recognition and face recognition algorithms are combined to implement automated access control at the gates of RAU campus. One image of the license plate and three images of the driver’s face are enough to check if the person driving a particular car into campus is the same as the person driving this car out. The license plate recognition module is based on learning vector quantization and performs well enough to be used in a realistic environment. The face recognition module is based on the Bayes rule and while performing satisfactory, extensive research is still necessary before this system can be implemented in real life. The main reasons for failure of the system were identified as the variable lighting and insufficient landmarks for effective warping.

This thesis presents the design of a recognizer for unconstrained handprinted alphanumeric characters. The design is based on a thinning process that is capable of producing thinned images with well defined features that are considered essential for character image description and recognition. By choosing the topological points of the thinned ('line') character image as these desired features, the thinning process achieves not only a high degree of data reduction but also transforms a binary image into a discrete form of line drawing that can be represented by graphs. As a result powerful graphical analysis techniques can be applied to analyze and classify the image. The image classification is performed in two stages. Firstly, a technique for identifying the topological points in the thinned image is developed. These topological points represent the global features of the image and because of their invariance to elastic deformations, they are used for image preclassification. Preclassification results in a substantial reduction in the entropy of the input image. The subsequent process can concentrate only on the differentiation of images that are topologically equivalent. In the preclassifier simple logic operations localized to the immediate neighbourhood of each pixel are used. These operations are also highly independent and easy to implement using VLSI. A graphical technique for image extraction and representation called the chain coded digraph representation is introduced. The technique uses global features such as nodes and the Freeman's chain codes for digital curves as branches. The chain coded digraph contains all the information that is present in the thinned image. This avoids using the image feature extraction approach for image description and data reduction (a difficult process to optimize) without sacrificing speed or complexity. After preclassification, a second stage of the recognition process analyses the chain coded digraph using the concept of attributed relational graph (ARG). ARG representation of the image can be obtained readily through simple transformations or rewriting rules from the chain coded digraph. The ARG representation of an image describes the shape primitives in the image and their relationships. Final classification of the input image can be made by comparing its ARG with the ARGs of known characters. The final classification involves only the comparison of ARGs of a predetermined topology. This information is crucial to the design of a matching algorithm called the reference guided inexact matching procedure, designed for high speed matching of character image ARGs. This graph matching procedure is shown to be much faster than other conventional graph matching procedures. The designed recognizer is implemented in Pascal on the PDP11/23 and VAX 11/750 computer. Test using Munson's data shows a high recognition rate of 91.46%. However, the recognizer is designed with the aim of an eventual implementation using VLSI and also as a basic recognizer for further research in reading machines. Therefore its full potential is yet to be realized. Nevertheless, the experiments with Munson's data illustrates the effectiveness of the design approach and the advantages it offers as a basic system for future research. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Tez (Yüksek Lisans) - Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik ve Haberleşme Mühendisliği Anabilim Dalı, 2007. / Kaynakça var.

The analysis and identification of texture is a key area in image processing and computer vision. One of the most prominent texture analysis algorithms is the Gabor Filter. These filters are used by convolving an image with a family of self similar filters or wavelets through the selection of a suitable number of scales and orientations, which are responsible for aiding in the identification of textures of differing coarseness and directions respectively. While extensively used in a variety of applications, including, biometrics such as iris and facial recognition, their effectiveness depend largely on the manual selection of different parameters values, i.e. the centre frequency, the number of scales and orientations, and the standard deviations. Previous studies have been conducted on how to determine optimal values. However the results are sometimes inconsistent and even contradictory. Furthermore, the selection of the mask size and tile size used in the convolution process has received little attention, presumably since they are image set dependent. This research attempts to verify specific claims made in previous studies about the influence of the number of scales and orientations, but also to investigate the variation of the filter mask size and tile size for water body extraction from satellite imagery. Optical satellite imagery may contain texture samples that are conceptually the same (belong to the same class), but are structurally different or differ due to changes in illumination, i.e. a texture may appear completely different when the intensity or position of a light source changes. A systematic testing of the effects of varying the parameter values on optical satellite imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to show the considerable changes in classification accuracy when varying the filter mask and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find optimum solutions in application domains where an enumeration approach is not feasible. Hence, the effectiveness of a GA to automate the process of determining optimum Gabor filter parameter values for a given image dataset is also investigated. The results of the research can be used to facilitate the selection of Gabor filter parameters for applications that involve multi-textured image segmentation or classification, and specifically to guide the selection of appropriate filter mask and tile sizes for automated analysis of satellite imagery. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.

Thesis (M. Eng. Sc.)--University of Adelaide, 1987. / Typescript. Includes a copy of a paper presented at TADSEM '85 --Australian Seminar on Devices for Expressive Communication and Environmental Control, co-authored by the author. Includes bibliographical references (leaves [115-121]).

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 208-226.