Background Quantifying joint deformity in people with rheumatoid arthritis (RA) and psoriatic arthritis (PsA), using high resolution peripheral quantitative computed tomography (HR-pQCT), remains problematic because it is difficult to estimate where the healthy joint surface would have been. Methods The second metacarpophalangeal of RA, PsA and healthy subjects were imaged with HR-pQCT. Using the bone surfaces of the healthy cohort as a reference, the method predicted the healthy surface of each individual diseased bone surface. Quantifiable outcomes were measured based on differences between the predicted healthy surface and the actual diseased surface. Sensitivity studies were conducted to measure precision, and the algorithm was validated against artificially created deformities with known geometries. Results Subjects with PsA and RA had significantly greater occurrences of erosion based surface outcomes than the healthy cohort. Sensitivity analyses revealed precision errors of up to 0.26 mm. Validating the algorithm showed an average accuracy error of 0.12 mm (4%) for detecting erosions and 0.27 mm (20%) for detecting periosteal bone growths. Conclusions The new method allows for visualization and quantification of surface changes within the affected joint by identifying areas of erosion and periosteal bone formation. Surface based outcomes are a novel way to interpret and further quantify articular bone changes affected by PsA and RA.

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration.