Currently the entire South African industry of Environmental Control Officers (ECOs) is unregulated, yet ECOs have the important task of checking and verifying compliance to environmental regulatory and performance requirements. According to international best practice principles for Environmental Impact Assessment (EIA), the process should be made credible through independent checks and verification (IAIA, 1999:3). According to the Environmental Impact Assessment Management Strategy Subtheme 5, quality assurance can only be attained when practitioners fulfil competence and ethical practice criteria. In this document the South African Department of Environmental Affairs (SADEA) also noted that there is currently no home for ECOs, yet this is where effective monitoring and enforcement could significantly improve environmental outcomes. The central objective of this dissertation was to investigate the need for regulation of the South African ECO industry. Data obtained from the questionnaires indicated that 100% of respondents were in agreement that there is a need for regulation of the South African ECO industry. One of the key motivational factors identified by respondents was quality assurance, which is important, as the environmental legislative regime changes constantly. Various other factors were identified other than quality assurance and were labelled “drivers”. These include establishment of minimum standards in respect of qualifications and/or experience (core competencies), establishment of a professional code of conduct and ethics that enhances accountability and professionalism, skills improvement through continual professional development (CPD), enhancement of credibility, independence of practitioners, enhancement of skills for capacity building, protection of clients against substandard work and overall lack of professionalism, and finally creation of a source of information support and interaction. It was important to determine what drives the regulation of an industry, in order to establish whether the South African ECO industry has similar drivers justifying regulation. The establishment of qualification and competency requirements was an important objective of this research, as a set of these requirements is an essential toolkit for operating ECOs and key stakeholders of the industry. Various registration and competence requirements for ECOs were identified from the literature review and responses to the questionnaires and interviews. With this research the author also intended to establish which current accreditation bodies could be considered for registration of ECOs and regulation of the South African ECO industry. The dissertation concludes by emphasising the importance of regulating the South African ECO industry, as this will ensure that compliance monitoring takes place effectively. / M Environmental Management, North-West University, Potchefstroom Campus, 2014

Medical imaging has seen a rapid development in its clinical use in assessment of treatment outcome, disease monitoring and diagnosis over the last few decades. Yet, the vast amount of available image data limits the practical use of this potentially very valuable source of information for radiologists and physicians. Therefore, the design of computer-aided medical image analysis is of great importance to imaging in clinical practice. This thesis deals with the problem of deformable image registration in the context of lung imaging, and addresses three of the major challenges involved in this challenging application, namely: designing an image similarity for multi-modal scans or scans of locally changing contrast, modelling of complex lung motion, which includes sliding motion, and approximately globally optimal mathematical optimisation to deal with large motion of small anatomical features. The two most important contributions made in this thesis are: the formulation of a multi-dimensional structural image representation, which is independent of modality, robust to intensity distortions and very discriminative for different image features, and a discrete optimisation framework, based on an image-adaptive graph structure, which enables a very efficient optimisation of large dense displacement spaces and deals well with sliding motion. The derived methods are applied to two different clinical applications in pulmonary image analysis: motion correction for breathing-cycle computed tomography (CT) volumes, and deformable multi-modal fusion of CT and magnetic resonance imaging chest scans. The experimental validation demonstrates improved registration accuracy, a high quality of the estimated deformations, and much lower computational complexity, all compared to several state-of-the-art deformable registration techniques.

This thesis addresses the problems of registration, localisation and simultaneous localisation and mapping (SLAM), relying particularly on local visual features extracted from camera images. These fundamental problems in mobile robot navigation are tightly coupled. Localisation requires a representation of the environment (a map) and registration methods to estimate the pose of the robot relative to the map given the robot’s sensory readings. To create a map, sensor data must be accumulated into a consistent representation and therefore the pose of the robot needs to be estimated, which is again the problem of localisation. The major contributions of this thesis are new methods proposed to address the registration, localisation and SLAM problems, considering two different sensor configurations. The first part of the thesis concerns a sensor configuration consisting of an omni-directional camera and odometry, while the second part assumes a standard camera together with a 3D laser range scanner. The main difference is that the former configuration allows for a very inexpensive set-up and (considering the possibility to include visual odometry) the realisation of purely visual navigation approaches. By contrast, the second configuration was chosen to study the usefulness of colour or intensity information in connection with 3D point clouds (“coloured point clouds”), both for improved 3D resolution (“super resolution”) and approaches to the fundamental problems of navigation that exploit the complementary strengths of visual and range information. Considering the omni-directional camera/odometry setup, the first part introduces a new registration method based on a measure of image similarity. This registration method is then used to develop a localisation method, which is robust to the changes in dynamic environments, and a visual approach to metric SLAM, which does not require position estimation of local image features and thus provides a very efficient approach. The second part, which considers a standard camera together with a 3D laser range scanner, starts with the proposal and evaluation of non-iterative interpolation methods. These methods use colour information from the camera to obtain range information at the resolution of the camera image, or even with sub-pixel accuracy, from the low resolution range information provided by the range scanner. Based on the ability to determine depth values for local visual features, a new registration method is then introduced, which combines the depth of local image features and variance estimates obtained from the 3D laser range scanner to realise a vision-aided 6D registration method, which does not require an initial pose estimate. This is possible because of the discriminative power of the local image features used to determine point correspondences (data association). The vision-aided registration method is further developed into a 6D SLAM approach where the optimisation constraint is based on distances of paired local visual features. Finally, the methods introduced in the second part are combined with a novel adaptive normal distribution transform (NDT) representation of coloured 3D point clouds into a robotic difference detection system.

Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2010. / Contains bibliography and index. / ENGLISH ABSTRACT: Super-resolution imaging is the process whereby several low-resolution photographs of an object are combined to form a single high-resolution estimation. We investigate each component of this process: image acquisition, registration and reconstruction. A new feature detector, based on the discrete pulse transform, is developed. We show how to implement and store the transform efficiently, and how to match the features using a statistical comparison that improves upon correlation under mild geometric transformation. To simplify reconstruction, the imaging model is linearised, whereafter a polygon-based interpolation operator is introduced to model the underlying camera sensor. Finally, a large, sparse, over-determined system of linear equations is solved, using regularisation. The software developed to perform these computations is made available under an open source license, and may be used to verify the results. / AFRIKAANSE OPSOMMING: In super-resolusie beeldvorming word verskeie lae-resolusie foto's van 'n onderwerp gekombineer in 'n enkele, hoë-resolusie afskatting. Ons ondersoek elke stap van hierdie proses: beeldvorming, -belyning en hoë-resolusie samestelling. 'n Nuwe metode wat staatmaak op die diskrete pulstransform word ontwikkel om belangrike beeldkenmerke te vind. Ons wys hoe om die transform e ektief te bereken en hoe om resultate kompak te stoor. Die kenmerke word vergelyk deur middel van 'n statistiese model wat bestand is teen klein lineêre beeldvervormings. Met die oog op 'n vereenvoudigde samestellingsberekening word die beeldvormingsmodel gelineariseer. In die nuwe model word die kamerasensor gemodelleer met behulp van veelhoek-interpolasie. Uiteindelik word 'n groot, yl, oorbepaalde stelsel lineêre vergelykings opgelos met behulp van regularisering. Die sagteware wat vir hierdie berekeninge ontwikkel is, is beskikbaar onderhewig aan 'n oopbron-lisensie en kan gebruik word om die gegewe resultate te veri eer.

The ability to synthesise the effects of ageing in human faces has numerous uses from aiding the search for missing people to improving recognition algorithms and aiding surgical planning. The principal contribution of this thesis is a novel method for synthesising the visual effects of facial ageing using a training set of three-dimensional scans to train a statistical ageing model. This data-base is constructed by fitting a statistical Face Model known as a Morphable Model to a set of two dimensional photographs of a set of subjects at different age points in their lives. We verify the effectiveness of this algorithm with both quantitative and psychological evaluation. Most ageing research has concentrated on building models using two-dimensional images. This has two major shortcomings, firstly some of the information related to shape change may be lost by the projection to two-dimensions; secondly the algorithms are very sensitive to even slight variations in pose and lighting. By using standard face-fitting methods to fit a statistical face model to the image we overcome these problems by reconstructing the lost shape information, and can use a model of physical rotations and light transfer to overcome the issues of pose and rotation. We show that the three-dimensional models captured by face-fitting offer an effective method of synthesising facial ageing. The second contribution is a new algorithm for ageing a face model based on Projection to Latent Structures also known as Partial Least Squares. This method attempts to separate the training set into a set of basis vectors that best explains the shape and colour changes related to ageing from those factors within the training set that are unrelated to ageing. We show that this method is more accurate than other linear techniques at producing a face model that resembles the individual at the target age and of producing a face image of the correct perceived age. The third contribution is a careful evaluation of three well known ageing methods. We use both quantitative evaluation to determine the accuracy of the ageing method, and perceptual evaluation to determine how well the model performs in terms of perceived age increase and also identity retention. We show that linear methods more accurately capture ageing and identity information if they are trained using an individualised model, and that ageing is more accurately captured if PLS is used to train the model.

Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.

Buildings and public infrastructures are crucial to our societies in that they provide habitations, workplaces, commodities and services indispensible to our daily life. As vital parts of facility management, operations and maintenance (O&M) ensure a facility to continuously function as intended, which take up the longest time in a facility’s life cycle and demand great expense. Therefore, computers and information technology have been actively adopted to automate traditional maintenance methods and processes, making O&M faster and more reliable. Augmented reality (AR) offers a new approach towards human-computer interaction through directly displaying information related to real objects that people are currently perceiving. People’s sensory perceptions are enhanced (augmented) with information of interest naturally without deliberately turning to computers. Hence, AR has been proved to be able to further improve O&M task performance. The research motif of this thesis is user evaluations of AR applications in the context of facility maintenance. The studies look into invisible target designation tasks assisted by developed AR tools in both indoor and outdoor scenarios. The focus is to examine user task performance, which is influenced by both AR system performance and human perceptive, cognitive and motoric factors. Target designation tasks for facility maintenance entail a visualization-interaction dilemma. Two AR systems built upon consumer-level hand-held devices using an off-the-shelf AR software development toolkit are evaluated indoors with two disparate solutions to the dilemma – remote laser pointing and the third person perspective (TPP). In the study with remote laser pointing, the parallax effect associated with AR “X-ray vision” visualization is also an emphasis. A third hand-held AR system developed in this thesis overlays infrared information on façade video, which is evaluated outdoors. Since in an outdoor environment marker-based tracking is less desirable, an infrared/visible image registration method is developed and adopted by the system to align infrared information correctly with the façade in the video. This system relies on the TPP to overcome the aforementioned dilemma.

In this paper I examine the factors that affect the likelihood an individual is a registered organ donor. Unlike many studies which focus on subpopulations in specific regions, I utilize national data to get a broader assessment of individuals from around the country across a number of racial and religious classifications. Using a probit model and controlling for a variety of parameters, I find that some racial and religious variables are negatively and significantly associated with organ donor registration rates, while education and being female are positively associated with organ donor registration rates. I conclude by discussing the implications of my results and the potential for future research.

Computational modelling and simulation, in close interaction with experiments, has provided invaluable insight into the biochemical, mechanical and electrophysiological function and dysfunction of the heart. However, limitations in imaging techniques and computing resources have precluded the analysis of tissue architecture near the cellular scale and the effect of this architecture on cardiac function. It is the wider aim of this thesis to develop a framework to characterise cardiac microstructure and to investigate the role of microstructure in cardiac propagation dynamics and arrhythmogenesis. An initial modelling study elucidates the effect of blood vessels in sustaining arrhythmic episodes, and how the accurate modelling of fibre direction in the vicinity of the vessels mitigates this detrimental mechanism. A mathematical model of fibre orientation in a simple geometry around blood vessels has been developed, based on information obtained from highly detailed histological and MRI datasets. A simulation regime was chosen, guided by the vasculature extracted from whole heart MRI images, to analyse ventricular wavefront propagation for different orientations and positions of blood vessels. Our results demonstrate not only that the presence of the blood vessels encourages curvature in the activation wavefront around the blood vessels, but further that vessels act to restrict and prolong phase singularities. When compared to a more simplistic implementation of fibre orientation, the model is shown to weaken wavefront curvature and reduce phase singularity anchoring. Having established the importance of microstructural detail in computational models, it seems expedient to generate accurate data in this regard. An automated registration toolchain is developed to reconstruct histological slices based on coherent block face volumes, in order to present the first 3-D sub-cellular resolution images of cardiac tissue. Although mesoscopic geometry is faithfully reproduced throughout much of the dataset, low levels of transformational noise obfuscate tissue microstructure. These distortions are all but eradicated by a novel transformational diffusion algorithm, with characteristics that outperform any previous method in the literature in this domain, with respect to robustness, conservation of geometry and extent of information transfer. Progress is made towards extracting microstructural models from the resultant histological volumes, with a view to incorporating this detail into simulations and yielding a deeper understanding of the role of microstructure in arrhythmia.

To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed “blocks”, are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.