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This paper presents an algorithm for a dollar bill recognition system. Although this thesis describes it in detail for the specific application of designing a dollar bill recognition system, the algorithm is quite general and can be applied to a variety of pattern recognition problems. The scheme operates on the image of a corner of the bill. Hough transform is used to find the edges and the corner point in the image. If there is any skew in the edges, it is corrected and a 256 x 256 pixel image is obtained. This image is then compressed to an 8 x 8 matrix, and features are extracted from a two dimensional Walsh Transform of this matrix. The process of feature selection is based upon the standard deviations of the Walsh coefficients. These features are then used by a Sequential Classifier for classifying the bill. / Master of Science

This work describes the design of an intelligent recognition system used to distinguish noise signatures of five different acoustic sources. The system uses pattern recognition techniques to identify the information obtained from a single microphone. A training phase is used in which the system learns to distinguish the sources and automatically selects features for optimal performance. Results were obtained by training the system to distinguish jet planes, propeller planes, a helicopter, train, and wind turbine from one another, then presenting similar sources to the system and recording the number of errors. These results indicate the system can successfully identify the trained sources based on acoustic information. Classification errors highlight the impact of the training sources on the system's ability to recognize different sources. / Master of Science

A system for the extraction and storage of line and region data from digitized engineering line drawings, first proposed by Watson et.al.[3] and further developed by Bixler et.al.[4], is completed. As a means for the automatic analysis of picture content, a model based recognizer for line patterns is developed. The pattern matcher uses a simple scheme to decompose a line drawing into basic parts: strokes and junctions, and then finds graph isomorphisms between known line pattern models stored in a database and portions of the image line data. Hu's moment invariants [16] are used to match simple shapes and prune the search space. Information about the connectivity of patterns matched in the image is retained, allowing higher level analysis of image content. A second method for calculating a moment signature from line data is presented. This method makes use of a spline approximation of the line data and Legendre polynomials. Some methods for recognizing incomplete line patterns and partially occluded curves are also discussed, and some experiments are performed. / M.S.

Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption. / Master of Science

Call number: LD2668 .T4 1985 S53 / Master of Science-

Thesis (MSc)--University of Stellenbosch, 2002. / ENGLISH ABSTRACT: The science of signature verification is concerned with identifying individuals by their handwritten signatures. It is assumed that the signature as such is a unique feature amongst individuals and the creation thereof requires a substantial amount of hidden information which makes it difficult for another individual to reproduce the signature. Modern technology has produced devices which are able to capture information about the signing process beyond what is visible to the naked eye. A dynamic signature verification system is concerned with utilizing not only visible, i.e. shape related information but also invisible, hidden dynamical characteristics of signatures. These signature characteristics need to be subjected to analysis and modelling in order to automate use of signatures as an identification metric. We investigate the applicability of hidden Markov models to the problem of modelling signature characteristics and test their ability to distinguish between authentic signatures and forgeries. / AFRIKAANSE OPSOMMING: Die wetenskap van handtekeningverifikasie is gemoeid met die identifisering van individue deur gebruik te maak van hulle persoonlike handtekening. Dit berus op die aanname dat 'n handtekening as sulks uniek is tot elke individu en die generering daarvan 'n genoeg mate van verskuilde inligting bevat om die duplisering daarvan moeilik te maak vir 'n ander individu. Moderne tegnologie het toestelle tevoorskyn gebring wat die opname van eienskappe van die handtekeningproses buite die bestek van visuele waarneming moontlik maak. Dinamiese handtekeningverifikasie is gemoeid met die gebruik nie alleen van die sigbare manefestering van 'n handtekening nie, maar ook van die verskuilde dinamiese inligting daarvan om dit sodoende 'n lewensvatbare tegniek vir die identifikasie van individue te maak. Hierdie sigbare en onsigbare eienskappe moet aan analise en modellering onderwerp word in die proses van outomatisering van persoonidentifikasie deur handtekeninge. Ons ondersoek die toepasbaarheid van verskuilde Markov-modelle tot die modelleringsprobleem van handtekeningkarakteristieke en toets die vermoë daarvan om te onderskei tussen egte en vervalste handtekeninge.