15 research outputs found

    Automated usability analysis and visualisation of eye tracking data

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    Usability is a critical aspect of the success of any application. It can be the deciding factor for which an application is chosen and can have a dramatic effect on the productivity of users. Eye tracking has been successfully utilised as a usability evaluation tool, because of the strong link between where a person is looking and their cognitive activity. Currently, eye tracking usability evaluation is a time–intensive process, requiring extensive human expert analysis. It is therefore only feasible for small–scale usability testing. This study developed a method to reduce the time expert analysts spend interpreting eye tracking results, by automating part of the analysis process. This was accomplished by comparing the visual strategy of a benchmark user against the visual strategies of the remaining participants. A comparative study demonstrates how the resulting metrics highlight the same tasks with usability issues, as identified by an expert analyst. The method also produces visualisations to assist the expert in identifying problem areas on the user interface. Eye trackers are now available for various mobile devices, providing the opportunity to perform large–scale, remote eye tracking usability studies. The proposed approach makes it feasible to analyse these extensive eye tracking datasets and improve the usability of an application.Dissertation (MSc)--University of Pretoria, 2014.Computer Scienceunrestricte

    Clearing the Clouds: Extracting 3D information from amongst the noise

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    Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

    Additive Manufacturing (AM) of Metallic Alloys

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    The introduction of metal AM processes in such industrial sectors as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight, and fewer potential failure points from joining features. This Special Issue on “Additive Manufacturing (AM) of Metallic Alloys” contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metals in AM

    Change blindness: eradication of gestalt strategies

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    Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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