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A reliable colour appearance model is desired by industry to achieve high colour fidelity between images produced using a range of different imaging devices. The aim of this study was to derive a reliable colour appearance model capable of predicting the change of perceived attributes of colour appearance under a wide range of media/viewing conditions. The research was divided into three parts: characterising imaging devices, conducting a psychophysical experiment, and developing a colour appearance model. Various imaging devices were characterised including a graphic art scanner, a Cromalin proofing system, an IRIS ink jet printer, and a Barco Calibrator. For the former three devices, each colour is described by four primaries: cyan (C), magenta (M), yellow (Y), and black (K). Three set of characterisation samples (120 and 31 black printer, and cube data sets) were produced and measured for deriving and testing the printing characterisation models. Four black printer algorithms (BPA), were derived. Each included both forward and reverse processes. A 2nd BPA printing model taking into account additivity failure, grey component replacement (GCR) algorithm gave the most accurate prediction to the characterisation data set than the other BPA models. The PLCC (Piecewise Linear interpolation assuming Constant Chromaticity coordinates) monitor model was also implemented to characterise the Barco monitor. The psychophysical experiment was conducted to compare Cromalin hardcopy images viewed in a viewing cabinet and softcopy images presented on a monitor under a wide range of illuminants (white points) including: D93, D65, D50 and A. Two scaling methods: category judgement and paired comparison, were employed by viewing a pair of images. Three classes of colour models were evaluated: uniform colour spaces, colour appearance models and chromatic adaptation transforms. Six images were selected and processed via each colour model. The results indicated that the BFD chromatic transform gave the most accurate predictions of the visual results. Finally, a colour appearance model, LLAB, was developed. It is a combination of the BFD chromatic transform and a modified version of CIELAB uniform colour space to fit the LUTCRI Colour Appearance Data previously accumulated. The form of the LLAB model is much simpler and its performance is more precise to fit experimental data than those of the other models.

The work presented in this thesis forms part of a research project which attempts to generate a visualisation of a speaker's mouth from purely acoustic speech signals. The aim is to provide an aid for partially hearing impaired people in which visual information is presented alongside limited acoustic signals, facilitating easier use of the telephone. The system is essentially a low-level speech recogniser in which phonemic information is extracted from the speech waveform and mapped onto visemes generated on a synthetic facial image. This thesis presents a description of a major part of this project, that is, the development of an accurate phoneme discriminator which is capable of speaker independent operation, on continuous speech. The recognition process is realised in three stages: a pre-processor to convert the speech into a suitable parametric form; a pattern recogniser to identify the possible phoneme classes and a post-processor to produce the viseme information. The pattern recognition stage uses a self-organising Kohonen network, followed by a Learning Vector Quantiser (LVQ) to further improve the recognition accuracy. The performance of this stage is highly dependent on the choice of pre-processor used at the input to the network and it is the design of the pre-processor stage that forms a significant part of this work. A novel technique known as the pseudo-cepstrum forms the basis of this pre-processor. Extensive investigations have been conducted into the dependence of performance on a range of parameters, both at the pre-processor stage and within the Kohonen classifier. In particular, a performance comparison of several preprocessor techniques, including the pseudo-cepstrum, has been carried out. Factors affecting both the training and operation of the classifier are also described here, with the sensitivity of recognition performance to the input data, being a major issue. Overall recognition accuracies of 80% have been achieved.

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The commercial exploitation of Electronic Speckle Pattern Interferometry (ESPI) is now gathering pace with manufacturers marketing products in Europe and the USA. The power of the technique both in a research and an industrial inspection role has brought pressure from the engineering community for an automated fringe analysis system.

In this research study, non-linear spectral mixing models have been developed and employed to achieve unmixing the proportions of components accurately and retrieving the physical parameters of the mixture. The first focus is on a comparative study of linear and non-linear spectral mixing models. A carefully-controlled experiment was conducted in the laboratory. The aim is to test both models to unmix ternary powdered-chalk mixtures by using the directional reflectance data. The results demonstrated the superiority of the non-linear model over the linear model. However, there was at least one case when the linear model produced more accurate results than the non-linear model. As a consequence, a hypothesis was made that the directional reflectance data obtained from certain measurement geometries may not contain useful information for deriving surface parameters. In order to investigate this, an error analysis was employed to observe the sensitivity to error of a physical parameter, which is needed in the non-linear unmixing, when estimated from directional reflectance data at certain measurement geometries. This theoretical investigation was tested against reflectance data of mineral mixtures obtained from a laboratory experiment. The results showed that the unmixing could be improved when the angular measurements were carefully chosen. Information contained in each surface measurement can be useful or damaging depending on the measurement geometry and the brightness of the surface itself. The next focus is on the utilisation of non-linear spectral mixing model to retrieve the biophysical properties of vegetation canopies by means of a canopy reflectance modelling. A two-layer model of the bidirectional reflectance of homogeneous vegetation canopies was proposed in this study. The anisotropic scatterings of both the vegetation canopy and the background were taken into account. This new model was validated against simulated and field-measurement data. The results showed that this model can be used to model the bidirectional reflectance and to retrieve the optical properties of canopy elements (leaves) and background of a homogeneous canopy. Finally, a simple non-linear spectral mixing model was developed. The second order interaction between vegetation and soil was taken into account. Results from the experiments showed that the vegetation cover and leaf area index of moderate density canopies can be retrieved by using this model.