Johan A. du Preez

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We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves(More)
We present two powerful tools which allow efficient training of arbitrary (including mixed and infinite) order hidden Markov models. The method rests on two parts: an algorithm which can convert high-order models to an equivalent first-order representation (ORder rEDucing), and a Fast (order) Incremental Training algorithm. We demonstrate that this method(More)
This paper introduces the first project of its kind within the Southern African language engineering context. It focuses on the role of idiosyncratic linguistic and pragmatic features of the different languages concerned and how these features are to be accommodated within (a) the creation of applicable speech corpora and (b) the design of the system at(More)
In audio-visual automatic speech recognition (AVASR) both acoustic and visual modalities of speech are used to identify what a person is saying. In this paper we propose a basic AVASR system implemented using SciPy, an open source Python library for scientific computing. AVASR research draws from the fields of signal processing, computer vision and machine(More)
Static signatures originate as handwritten images on documents and by definition do not contain any dynamic information. This lack of information makes static signature verification systems significantly less reliable than their dynamic counterparts. This study involves extracting dynamic information from static images, specifically the pen trajectory while(More)
When a large number of documents, e.g. bank cheques, have to be authenticated in a limited time, the manual verification of, say the authors’ signatures, is often unrealistic. This led to the development of a wide range of automatic off-line signature verification systems. However, the value of such a system is rarely demonstrated by conducting a subjective(More)
BACKGROUND This paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult. METHODS The endo- and epicardium are modelled as two series of radii which are(More)