3D Object Segmentation of Point Clouds using Profiling Techniques

In the automatic processing of point clouds, higher level information in the form of point segments is required for classification and object detection purposes. Segmentation allows for the definition of these segments. Because of the increasing size of point clouds faster and more reliable segmentation methods are being sought. Various algorithms have been proposed for the segmentation of point clouds. In this paper, an extension of a segmentation approach based on intersecting profiles is proposed. In the presented method, surfaces are considered as a graph of intersecting planar curves. In this graph structure curves intersect at common points and terminate at surface discontinuities. This property of the curves makes it possible to determine point segments by connected components. A method for the detection of curves in the profiles is presented. The algorithm has been tested on terrestrial lidar point clouds

Short communication: Cyclodextrin nanosponges in the removal of organic matter to produce water for power generation

The water treatment processes employed by coal-fired power stations do not completely remove most of the natural organics (volatile component) from the feed water used for power generation. Currently, polyaluminium chloride, polyelectrolyte and ion exchange resins are used to treat water at power stations. The effectiveness of water-insoluble cyclodextrin (CD) polymers in the removal of natural organics (volatile component), dissolved organic carbon (DOC) and total organic carbon (TOC) from water collected at a specific power plant is reported. Results obtained from this study show that, despite the usage of the treatment processes, natural organic species emanating from raw water still persist throughout the stages of the water treatment process. The polymers on the other hand demonstrated the ability to remove dissolved organic carbon (DOC) from raw water by as much as 84%, whilst TOC removal was relatively low.Keywords: organic pollutants, dissolved organic carbon (DOC), total organic carbon (TOC), cyclodextrin polymers, coagulants, ion-exchange resi

An investigation into how post office managers perceive the effectiveness of change.

Thesis (MBA)-University of Natal, Durban, 2002.No abstract available

Improving 3d pedestrian detection for wearable sensor data with 2d human pose

Collisions and safety are important concepts when dealing with urban designs like shared spaces. As pedestrians (especially the elderly and disabled people) are more vulnerable to accidents, realising an intelligent mobility aid to avoid collisions is a direction of research that could improve safety using a wearable device. Also, with the improvements in technologies for visualisation and their capabilities to render 3D virtual content, AR devices could be used to realise virtual infrastructure and virtual traffic systems. Such devices (e.g., Hololens) scan the environment using stereo and ToF (Time-of-Flight) sensors, which in principle can be used to detect surrounding objects, including dynamic agents such as pedestrians. This can be used as basis to predict collisions. To envision an AR device as a safety aid and demonstrate its 3D object detection capability (in particular: pedestrian detection), we propose an improvement to the 3D object detection framework Frustum Pointnet with human pose and apply it on the data from an AR device. Using the data from such a device in an indoor setting, we conducted a comparative study to investigate how high level 2D human pose features in our approach could help to improve the detection performance of orientated 3D pedestrian instances over Frustum Pointnet

Traffic Control Recognition with AN Attention Mechanism Using Speed-Profile and Satellite Imagery Data

Traffic regulators at intersections act as an essential factor that influences traffic flow and, subsequently, the route choices of commuters. A digital map that provides up-to-date traffic control information is beneficial not only for facilitating the commuters’ trips, but also for energy-saving and environmental protection. In this paper, instead of using expensive surveying methods, we propose an automatic way based on a Conditional Variational Autoencoder (CVAE) to recognize traffic regulators, i. e., arm rules at intersections, by leveraging the GPS data collected from vehicles and the satellite imagery retrieved from digital maps, i. e., Google Maps. We apply a Long Short-Term Memory to extract the motion dynamics over a GPS sequence traversed through the intersection. Simultaneously, we build a Convolutional Neural Network (CNN) to extract the grid-based local imagery information associated with each step of the GPS positions. Moreover, a self-attention mechanism is adopted to extract the spatial and temporal features over both the GPS and grid sequences. The extracted temporal and spatial features are then combined for detecting the traffic arm rules. To analyze the performance of our method, we tested it on a GPS dataset collected by driving vehicles in Hannover, a medium-sized German city. Compared to a Random Forest model and an Encoder-Decoder model, our proposed model achieved better results with both accuracy and F1-score of 0.90 for the three-class (arm rules of uncontrolled, traffic light, and priority sign) task. We also carried out ablation studies to further investigate the effectiveness of the GPS input branch, the image input branch, and the self-attention mechanism in our model

Practices in scholarly publishing : making sense of rejection

Abstract: In South Africa, criticisms of peer review often hinge on allegations of racism, anti-African attitudes, and viewpoint discrimination. This article discusses the issue of peer-review, and examines these allegations in terms of claims of Western conceptual gatekeeping. Cautions are offered on allegations of exceptionalism, as are some strategies on dealing with the process of peer review

Contribution of Baobab Production Activities to Household Livelihoods.

IES Working paper.Baobab production activities play a crucial role in contributing to the livelihoods of rural households. In the face of increasing village populations, commercial use of baobab has been steadily increasing to the point where currently, 43% of sampled households participate in baobab production activities. Commercial use of baobab products is especially important to the poorer households arid women. In terms of contributing to household livelihoods, baobab activities are ranked second only to some kinds of agricultural production. Numerical estimates of contribution to livelihoods bear out this result with cash income of approximately Z$5000 per annum received for each participating person, well above the official minimum wage. Opportunity costs of labour make up about four-fifths of this value, leaving one-fifth of the cash income accruing as economic rent. The rent available to households seems to vary widely, as there are households that are well located close to baobab trees, which greatly reduces production costs and increases economic rents captured. The importance of baobabs to livelihoods, combined with the potential ecological importance of these trees in contributing to biodiversity, makes the sustainability of this resource vital. Accordingly, if current use rates are not sustainable (see Romero et al., (in prep) there is scope for investigations into policies and management options that could foster sustainable use

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Barriers and facilitators to infection prevention and control in a neonatal unit in Zimbabwe – a theory-driven qualitative study to inform design of a behaviour change intervention

BACKGROUND: Hospital-acquired infection (HAI) is an increasing cause of neonatal morbidity/mortality in low-income settings. Hospital staff behaviours (e.g. hand hygiene) are key contributors to HAI. Understanding the drivers of these can inform interventions to improve infection prevention and control (IPC). AIM: To explore barriers/facilitators to IPC in a neonatal unit in Harare, Zimbabwe. METHODS: Interviews were conducted with fifteen staff members of neonatal and maternity units alongside ethnographic observations. The interview guide and data analysis were informed by the COM-B (Capability, Opportunity, Motivation-Behaviour) model and explored individual, socio-cultural, and organisational barriers/facilitators to IPC. Potential interventions were identified using the Behaviour-Change Wheel. FINDINGS: Enablers within Capability included awareness of IPC, and within Motivation beliefs that IPC was crucial to one's role, and concerns about consequences of poor IPC. Staff were optimistic that IPC could improve, contingent upon resource availability (Opportunity). Barriers included: limited knowledge of guidelines, no formal feedback on performance (Capability), lack of resources (Opportunity), often leading to improvisation and poor habit formation. Further barriers included the unit's hierarchy e.g. low engagement of cleaners and mothers in IPC, and staff witnessing implementation of poor practices by other team members (Opportunity). Potential interventions could include role-modelling, engaging mothers and staff across cadres, audit and feedback and flexible protocols (adaptable to water/handrub availability). CONCLUSIONS: Most barriers to IPC fell within Opportunity, whilst most enablers fell under Capability and Motivation. Theory-based investigation provides basis for systematically identifying and developing interventions to address barriers and enablers to IPC in low-income settings

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%