This dissertation examines the suitability of Display-Processor (DP) image computers for image enhancement and restoration tasks. Because the major architectural feature of the DP devices is their ability to rapidly evaluate finite impulse response (FIR) convolutions, much of the study focusses on the use of spatial-domain FIR convolutions to approximate Fourier-domain filtering. When the enhancement task requires the evaluation of only a single convolution, it is important that the FIR kernel used to implement the convolution is designed so that the resulting output is a good approximation of the desired output. A Minimum-Mean-Squared-Error design criterion is introduced for the purpose of FIR kernel design and its usefulness is demonstrated by showing some results of its use. If the restoration or enhancement task requires multiple convolutions in an iterative algorithm, it is important to understand how the truncation of the kernel to a finite region of support will affect the convergence properties of an algorithm and the output of the iterative sequence. These questions are examined for a limited class of nonlinear restoration algorithms. Because FIR convolutions are most efficiently performed on computing machines that have limited precision and are usually limited to performing fixed-point arithmetic, the dissertation also examines the effects of roundoff error on output images that have been computed using fixed point math. The number of bits that are needed to represent the data during a computation is algorithm dependent, but for a limited class of algorithms, it is shown that 12 bits are sufficient. Finally, those architectural features in a DP that are necessary for useful enhancement and restoration operations are identified.

published_or_final_version / Electrical Engineering / Master / Master of Philosophy

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Digital image feature detectors often comprise two stages of processing: an initial filtering phase and a secondary search stage. The initial filtering is designed to accentuate specific feature characteristics or suppress spurious components of the image signal. The second stage of processing involves searching the results for various criteria that will identify the locations of the image features. The local energy feature detection scheme combines the squares of the signal convolved with a pair of filters that are in quadrature with each other. The resulting local energy value is proportional to phase congruency which is a measure of the local alignment of the phases of the signals constituent Fourier components. Points of local maximum phase alignment have been shown to correspond to visual features in the image. The local energy calculation accentuates the location of many types of image features, such as lines, edges and ramps and estimates of local energy can be calculated in multidimensional image data by rotating the quadrature filters to several orientations. The second stage search criterion for local energy is to locate the points that lie along the ridges in the energy map that connect the points of local maxima. In three dimensional data the relatively higher energy values will form films between connecting laments and tendrils. This thesis examines the use of recursive spatial domain filtering to calculate local energy. A quadrature pair of filters which are based on the first derivative of the Gaussian function and its Hilbert transform, are rotated in space using a kernel of basis functions to obtain various orientations of the filters. The kernel is designed to be separable and each term is implemented using a recursive digital filter. Once local energy has been calculated the ridges and surfaces of high energy values are determined using a flooding technique. Starting from the points of local minima we perform an ablative skeletonisation of the higher energy values. The topology of the original set is maintained by examining and preserving the topology of the neighbourhood of each point when considering it for removal. This combination of homotopic skeletonisation and sequential processing of each level of energy values, results in a well located, thinned and connected tracing of the ridges. The thesis contains examples of the local energy calculation using steerable recursive filters and the ridge tracing algorithm applied to two and three dimensional images. Details of the algorithms are contained in the text and details of their computer implementation are provided in the appendices.

Thesis (DTech(Business Informatics))--Cape Technikon, Cape Town, 1997 / Modem applications in computer graphics and telecommunications command high performance filtering and smoothing to be implemented. The recent development of a new class of max-min selectors for digital image processing is investigated with special emphasis on the practical implications for hardware and software design.

Many applications of image processing require the initial segmentation of the image by means of grey-level thresholding. In this thesis, the problems of automatic threshold selection and evaluation are addressed in order to find a universally applicable thresholding method. Three previously proposed threshold selection techniques are investigated, and two new methods are introduced. The results of applying these methods to several different images are evaluated using two threshold evaluation techniques, one subjective and one quantitative. It is found that no threshold selection technique is universally acceptable, as different methods work best with different images and applications

The question to be answered by this research is how would a fine artist working in South Africa, identify and use the correct digital printing techniques to accurately print their work?

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M.Ing. / This dissertation proposes a marker-free visual based interface device to be used with menu driven systems. This system, called HandsFree, uses the Graphics Processing Unit (GPU) together with Shader technology to perform the image processing. HandsFree makes use of a web camera to gain user input without requiring elementary computer skills. Background subtraction was used to extract user input from the images. The problems usually obtained with background subtraction were overcome by using an averaging technique. Test results proved HandsFree to be robust against different coloured backgrounds and skin tones, different lighting intensity and sudden change in lighting intensity.