Motion capture technology is recognised as a standard tool in the computer animation pipeline. It provides detailed movement for animators; however, it also introduces problems and brings concerns for creating realistic and convincing motion for character animation. In this thesis, the post-processing techniques are investigated that result in realistic motion generation. Anumber of techniques are introduced that are able to improve the quality of generated motion from motion capture data, especially when integrating motion transitions from different motion clips. The presented motion data reconstruction technique is able to build convincing realistic transitions from existing motion database, and overcome the inconsistencies introduced by traditional motion blending techniques. It also provides a method for animators to re-use motion data more efficiently. Along with the development of motion data transition reconstruction, the motion capture data mapping technique was investigated for skeletal movement estimation. The per-frame based method provides animators with a real-time and accurate solution for a key post-processing technique. Although motion capture systems capture physically-based motion for character animation, no physical information is included in the motion capture data file. Using the knowledge of biomechanics and robotics, the relevant information for the captured performer are able to be abstracted and a mathematical-physical model are able to be constructed; such information is then applied for physics-based motion data correction whenever the motion data is edited.

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This thesis is concerned with the investigation of methods of providing support to non-expert users of telecare systems by creating easy-to-use interfaces and assessing the effect of adapting the interface to suit the cognitive style of individual users. The contributions to knowledge fall into three main areas; firstly the innovations built into a prototype adaptive telecare system, secondly the identification of the sort of tasks and the types of media that best suit different cognitive style groups, and thirdly the proposal of a new dimension of cognitive style that classifies individuals by their perception of visual compared to auditory information. The first phase of the project is concerned with the design and implementation of a prototype adaptive telecare system that demonstrates existing usability principles. The prototype system enables users to communicate over the Internet using text, audio and video, and to enable access to information stored within the system and on the Internet. The adaptive features include the automatic selection of information based on the knowledge of the user and the automatic selection of a presentation style that is based on the way the user perceives information. The system contains a number of innovations that relate to the application of the technology used to build the system, how information is structured, and the design of the style of interaction. The second phase of the project is concerned with assessing the effect of designing interfaces using different media that are suitable for individual users based on how they perceive and process information. Cognitive style is found to significantly affect performance in few tasks, but relative differences of performance are observed between the cognitive style groups in the different types of task and in the different media versions of each task. A major contribution to knowledge is the identification of the tasks and the types of media that suit different cognitive style groups, as this will help developers of multimedia systems to design interfaces that will improve the performance ofusers in each cognitive style group. The major contribution to the field is the proposal of a new visual-auditory dimension of cognitive style. The assessment of cognitive style using a visual test is found to be significantly different to an assessment using an auditory test. An individual's style can be calculated using an existing cognitive styles analysis test augmented by the new audio test presented in chapter 8. The new visual and auditory classification of cognitive style is found to explain the performance of subjects to a greater degree than the old purely visual classification.

The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined forecast quality. Three types of diversification are used: (a) diversification concerning the level of learning (b) diversification of predefined parameter values and (c) the use of different forecast models. The diversification is carried out on forecasts of seasonal factor predictions in Revenue Management for Airlines. After decomposing the data and generating diversified forecasts a (multi step) combination procedure is applied. We provide theoretical evidence of why and under which conditions multi step multi level forecast combination can be a powerful approach in order to build a high quality and adaptive forecast system. We theoretically and experimentally compare models differing with respect to the used decomposition, diversification as well as the applied combination models and structures. After an introduction into the application of forecasting seasonal behaviour in Revenue Management, a literature review of the theory of forecast combination is provided. In order to get a clearer idea of under which condition combination works, we then investigate aspects of forecast diversity and forecast diversification. The diversity of forecast errors in terms of error covariances can be expressed in a decomposed manner in relation to different independent error components. This type of decomposed analysis has the advantage that it allows conclusions concerning the potential of the diversified forecasts for future combination. We carry out such an analysis of effects of different types of diversification on error components corresponding to the bias-variance-Bayes decomposition proposed by James and Hastie. Different approaches of how to include information from different levels into forecasting are also discussed in the thesis. The improvements achieved with multi level forecast combination prove that theoretical analysis is extremely important in this relatively new field. The bias-variance-Bayes decomposition is extended to the multi level case. An analysis of the effects of including forecasts with parameters learned at different levels on the bias and variance error components show that forecast combination is the best choice in comparison to some other discussed alternatives. The proposed approach represents a completely automatic procedure. It realises changes in the error components which are not only advantageous at the low level, but have also a stabilising effect on aggregates of low level forecasts to the higher level. We also identify cases in which multi level forecast combination should ideally be connected with the use of different function spaces and/or thick modelling related to certain parameter values or preprocessing procedures. In order to avoid problems occurring for large sets of highly correlated forecasts when considering covariance information, we investigated the potential of pooling and trimming for our case. We estimate the expected behaviour of our diversified forecasts in purely error variance based pooling represented by a common approach of Aiolfi and Timmermann and analyse effects of different kinds of covariances on the accuracy of the combined forecast. We show that a significant loss in the expected forecast accuracy may ensue because of typical inhomogeneities in the covariance matrix for the analysed case. If covariance information is available in a sufficiently high quality, it is possible to run a clustering directly based on covariance information. We discuss how to carry out a clustering in that case. We also consider a case (quite common in our application) when covariance information may not be available and propose a novel simplified representation of the covariance matrix which represents the distance in the forecast generation space and is only based on knowledge about the forecast generation process. A new pooling approach is proposed that avoids inhomogeneities in the covariance matrix by considering the information contained in the simplified covariance representation. One of the main advantages of the proposed approach is that the covariance matrix does not have to be calculated. We compared the results of our approach with the approach of Aiolfi and Timmermann and explained the reasons for significant improvement. Another advantage of our approach is that it leads to the generation of novel multi step, multi level forecast generation structures that carry out the combination in different steps of pooling. Finally, we describe different evolutionary approaches in order to generate combination structures automatically. We investigate very flexible approaches as well as approaches that avoid the expected inhomogeneities in the error covariance matrix based on our theoretical findings. The theoretical analysis is supported by experimental results. We could achieve an improvement of forecast quality up to 11 percent for the practical application of demand forecasting in Revenue Management compared to the current optimised forecasting system.

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For most people, the creation and editing of mathematical text in electronic documents is a slow, tedious and error-prone activity. For people with disabilities, especially blindness or severe visual impairments, this is far more of a problem. The lack of easy access to good mathematical resources limits the educational and career opportunities for people with such disabilities. Automatic Speech Recognition (ASR) could enable both able-bodied and people who are physically disabled gain better access to mathematics. However, whilst ASR has improved over recent years, most speech recognition systems do not support the input and editing of dictated mathematical expressions. In this thesis, we present results of studies of how students and staff at Kingston University, of various levels of mathematical achievement, read-out given expressions in English. Furthermore, we analyse evidence, both from our own studies, and from transcriptions of mathematics classes recorded in the British National Corpus (BNC), that people do consistently place pauses to mark the grouping of subexpressions. The results from this enabled us to create an innovative context-free attribute grammar capable of capturing a high proportion of GCSE-Ievel spoken mathematics, of which can be syntactically incorrect and/or incomplete. This attribute grammar was implemented, tested and evaluated in our prototype system TalkMaths. We also compiled statistics of "common sequences" of mathematics-related keywords from these two sources, with a view to using these to develop a "predictive model" for use in our system. We implemented and evaluated a prototype system TalkMaths, that enables the dictation of mathematical expressions, up to approximately GCSE level, and converts them into various electronic document formats Our evaluations of this system showed that people of various levels of mathematical ability can learn how to produce electronic mathematical documents by speech. These studies have demonstrated that producing mathematical documents by speech is a viable alternative to using the keyboard & mouse, especially for those who rely on speech recognition software to use a computer. A novel editing paradigm, based on a "hybrid grid" is proposed, implemented and tested in a further usability study. Although the evaluation of this editing paradigm is incomplete, it has demonstrated that it is promising and worthy of further research.

Diabetic retinopathy (DR) is a retinal vascular disease associated with diabetes and it is one of the most common causes of blindness worldwide. Diabetic patients regularly attend retinal screening in which digital retinal images are captured. These images undergo thorough analysis by trained individuals, which can be a very time consuming and costly task due to the large diabetic population. Therefore, this is a field that would greatly benefit from the introduction of automated detection systems. This project aims to automatically detect proliferative diabetic retinopathy (PDR), which is the most advanced stage of the disease and poses a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. Their tortuous, convoluted and obscure appearance can make them difficult to detect. In this thesis, we present a methodology based on the novel approach of creating two different segmented vessel maps. Segmentation methods include a standard line operator approach and a novel modified line operator approach. The former targets the accurate segmentation of new vessels and the latter targets the reduction of false responses to non-vessel edges. Both generated binary vessel maps hold vital information which is processed separately using a dual classification framework. Features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. The proposed methodology, using a dataset of 60 images, achieves a sensitivity of 100.00% and a specificity of 92.50% on a per image basis and a sensitivity of 87.93% and a specificity of 94.40% on a per patch basis. The thesis also presents an investigation into the search for the most suitable features for the classification of PDR. This entails the expansion of the feature vector, followed by feature selection using a genetic algorithm based approach. This provides an improvement in results, which now stand at a sensitivity and specificity 3 of 100.00% and 97.50% respectively on a per image basis and 91.38% and 96.00% respectively on a per patch basis. A final extension to the project sees the framework of dual classification further explored, by comparing the results of dual SVM classification with dual ensemble classification. The results of the dual ensemble approach are deemed inferior, achieving a sensitivity and specificity of 100.00% and 95.00% respectively on a per image basis and 81.03% and 95.20% respectively on a per patch basis.

The advancements in multimedia communication technologies have enabled an increased implementation of telemedicine and e-health application for healthcare services. In parallel, advanced imaging methods have facilitated increasing reliance on medical images and videos for patient diagnosis. The high data speeds achieved by current communication technologies enables reliable transmission of medical videos for diagnosis and education purposes in telemedicine applications. The necessary process of video compression, prior to transmission, and communication channel constraints may occasionally impact the quality of the medical video received after transmission. Thus, to verify the reliability of the received video, quality evaluation is necessary. However, the present approaches used for medical video quality evaluation have limitations in addressing the contextual requirements of medical videos. The research work presented in this thesis addresses quality evaluation of medical ultrasound videos for telemedicine and e-health applications. The studies presented in the thesis include a subjective quality assessment study of medical ultrasound videos compressed via the High Efficiency Video Coding (HEVC) standard and the validation of the performance of state-of-the-art video quality metrics using the subjective cores of medical experts. Further, the rate-distortion and rate-quality performance of HEVC is analysed for the compression of medical ultrasound videos. A video quality metric, Cardiac Ultrasound Quality Index (CUQI), for cardiac ultrasound videos is proposed that considers the motion and edge features of cardiac videos for quality evaluation. The proposed metric assessment closely agrees with the subjective assessment of medical experts. Finally, a content-aware packet scheduling approach for transmission of medical ultrasound videos over Long Term Evolution (LTE) wireless network is presented. The scheduling approach employs a utility function based on the temporal complexity of the medical ultrasound videos and results in improving the received video quality. The research outcomes presented in the thesis indicate that developing quality evaluation approaches according to the contextual requirements of the medical video modality could enable overcoming the limitations of standard quality evaluation approaches.

The human retina has the potential to reveal important information about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. Automatic quantification of retinal vessel morphology and width is considered as a first step in computer assisted medical applications related to diagnosis and treatment planning. This work aims to quantify the blood vessels in noisy and pathological retinal images of school children with uneven illumination and containing complex vessel profiles. In this thesis, we have presented two methodologies of retinal vessel segmentation and an algorithm for vessel width measurement. The unsupervised method of retinal segmentation is based on detection of vessel centrelines and followed by computing the vessel shape and the orientation map using morphological bitplane slicing. A supervised method for segmentation of blood vessels by using an ensemble classifier of boosted and bagged decision trees is also presented. The feature vector encodes information to successfully handle both normal and pathological retinas with bright and dark lesions simultaneously. The obtained performance metrics illustrate that this method outperforms most of the state-of-the-art methodologies of retinal vessel segmentation. The method is computationally fast in training and classification and needs fewer samples for training than other supervised methods. It is training set robust as it offers a better performance even when it is trained and tested on different sets of retinal images. A new public database of the retinal images taken from multi-ethnic school children is presented along with the ground truths of vessel segmentation and width measurement. We have also introduced a robust and accurate methodology for measuring the calibre of vessel segments in retinal images of multi-ethnic children. The vessel centrelines are detected from the vessel probability map image resulting from ensemble classification. The vessel branch points and crossovers are identified and removed from the vessel centreline image to obtain vessel segments followed by computing the local vessel orientation of the vessel segments. The width of each vessel segment is estimated using a two dimensional model with incorporated Gaussian (for ordinary vessels) as well as Difference of Gaussian profiles (for vessels with a central reflex). The automated methods for quantification of retinal vessel morphology and width may be used as an alternative to the time consuming subjective clinical evaluation for monitoring the progression of retinopathies and their association with normal and abnormal vascular patterns. This may enable a quick diagnosis, treatment availability, prognosis, and facilitation of clinical heath-care procedures in remote areas.