There are many different modelling techniques used in computer graphics to describe a wide range of objects and phenomena. In this thesis, details of research into the isosurface modelling technique are presented. The isosurface technique is used in conjunction with more traditional modelling techniques to describe the objects needed in the different scenes of an animation. The isosurface modelling technique allows the description and animation of objects that would be extremely difficult, or impossible to describe using other methods. The objects suitable for description using isosurface modelling are soft objects. Soft objects merge elegantly with each other, pull apart, bubble, ripple and exhibit a variety of other effects. The representation was studied in three phases of a computer animation project: modelling of the objects; animation of the objects; and the production of the images. The research clarifies and presents many algorithms needed to implement the isosurface representation in an animation system. The creation of a hierarchical computer graphics animation system implementing the isosurface representation is described. The scalar fields defining the isosurfaces are represented using a scalar field description language, created as part of this research, which is automatically generated from the hierarchical description of the scene. This language has many techniques for combining and building the scalar field from a variety of components. Surface attributes of the objects are specified within the graphics system. Techniques are described which allow the handling of these attributes along with the scalar field calculation. Many animation techniques specific to the isosurface representation are presented. By the conclusion of the research, a graphics system was created which elegantly handles the isosurface representation in a wide variety of animation situations. This thesis establishes that isosurface modelling of soft objects is a powerful and useful technique which has wide application in the computer graphics community.

This present research is concerned with the design, development and implementation of three-dimensional computer-generated facial images capable of expression gesture and speech. A review of previous work in chapter one shows that to date the model of computer-generated faces has been one in which construction and animation were not separated and which therefore possessed only a limited expressive range. It is argued in chapter two that the physical description of the face cannot be seen as originating from a single generic mould. Chapter three therefore describes data acquisition techniques employed in the computer generation of free-form surfaces which are applicable to three-dimensional faces. Expressions are the result of the distortion of the surface of the skin by the complex interactions of bone, muscle and skin. Chapter four demonstrates with static images and short animation sequences in video that a muscle model process algorithm can simulate the primary characteristics of the facial muscles. Three-dimensional speech synchronization was the most complex problem to achieve effectively. Chapter five describes two successful approaches: the direct mapping of mouth shapes in two dimensions to the model in three dimensions, and geometric distortions of the mouth created by the contraction of specified muscle combinations. Chapter six describes the implementation of software for this research and argues the case for a parametric approach. Chapter seven is concerned with the control of facial articulations and discusses a more biological approach to these. Finally chapter eight draws conclusions from the present research and suggests further extensions.

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Problems associated with the processing and statistical analysis of image data are the subject of much current interest, and many sophisticated techniques for extracting semantic content from degraded or corrupted images have been developed. However, such techniques often require considerable computational resources, and thus are, in certain applications, inappropriate. The detection localised discontinuities, or edges, in the image can be regarded as a pre-processing operation in relation to these sophisticated techniques which, if implemented efficiently and successfully, can provide a means for an exploratory analysis that is useful in two ways. First, such an analysis can be used to obtain quantitative information relating to the underlying structures from which the various regions in the image are derived about which we would generally be a priori ignorant. Secondly, in cases where the inference problem relates to discovery of the unknown location or dimensions of a particular region or object, or where we merely wish to infer the presence or absence of structures having a particular configuration, an accurate edge-detection analysis can circumvent the need for the subsequent sophisticated analysis. Relatively little interest has been focussed on the edge-detection problem within a statistical setting. In this thesis, we formulate the edge-detection problem in a formal statistical framework, and develop a simple and easily implemented technique for the analysis of images derived from two-region single edge scenes. We extend this technique in three ways; first, to allow the analysis of more complicated scenes, secondly, by incorporating spatial considerations, and thirdly, by considering images of various qualitative nature. We also study edge reconstruction and representation given the results obtained from the exploratory analysis, and a cognitive problem relating to the detection of objects modelled by members of a class of simple convex objects. Finally, we study in detail aspects of one of the sophisticated image analysis techniques, and the important general statistical applications of the theory on which it is founded.

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Recognising faces is a task taken for granted by most people, yet it probably represents one of the most complicated visual tasks we routinely perform. Progress in machine vision over recent years has been considerable, but has generally concentrated on areas inappropriate to face recognition. Faces are soft and round, lacking the clear edges and strong geometric properties usually required for machine vision. Instead, subtle changes in shading and texture indicate the transition from one <i>feature</i> to another. To compound the problem, faces are generally very similar, and the small differences that do exist are significant. We describe a machine vision system, called FindFace, that makes use of the underlying similarity of faces to locate specific features, such as the eyes and the mouth. Statistics gathered from 1000 faces are used both to predict the location of features, and evaluate locations generated by numerous independent feature locating routines, called <i>experts</i>. Once an initial location is determined, predictions about the positions of other features can be investigated. This can lead to a rapid increase in confidence as other features are identified in their predicted position, or alternativley to the initial location being quickly rejected. Individual experts can be simple, as a supervisory control system evaluates their performance using the face statistics, and can distinguish good results from bad. The control system can utilise multiple experts for individual features, selecting the most appropriate dynamically based on their previous success rate. The interface between experts and the control system is simple, making the addition of new experts easy. The combination of detailed statistics with many feature experts results in a system that is unhindered by failure to locate specific features, and that continues serching for features until the best solution is obtained with the experts available.

Field Cycled Magnetic Resonance Imaging offers potential for significant Signal to Noise Ratio and T₁ contrast improvements of Magnetic Resonance images. In this work the hardware and software components of a home made, low field NMR imager were modified, in order to investigate Field Cycling. Theoretical models were developed to simulate NMR signal response to different magnetic field pulse shapes and it was seen that trapezoidal magnetic field pulse waveforms, with rise/fall times considerably smaller than the T₁ relaxation times of the examined samples, give comparable results to those of an ideal, rectangular pulse. The steady state signal expressions of Field Cycled Gradient Echo and Spin Echo sequences were derived and tested experimentally. Differences between theoretical and experimental results can be attributed to the increased levels of noise and the effect of time dependent magnetic fields due to limited efficiency of the induced current cancelling scheme. The ability of the imager to measure T₁ relaxation times at different magnetic field strengths, employing Field Cycled imaging sequences, was also assessed. Results were again burdened by the above mentioned problems, as well as, by the considerable temperature changes the samples suffered during the long imaging times required for complete study.