A long standing problem in computer graphics and animation is the production of synthetic computer graphics models whose appearance, movement and behaviour are visually indistinguishable from the real world. This thesis addresses the problem of reconstructing visually realistic computer graphics models of real people using multiple camera views. A model-based computer vision algorithm is introduced to reconstruct the shape and appearance of a person in an arbitrary pose viewed in a multiple camera studio. Current techniques for multiple view reconstruction address the problem of general scene recovery. These non model-based approaches can fail to accurately reconstruct shape and appearance in the presence of visual ambiguities. The techniques also provide no structure to edit or reuse the captured content in computer animation. The primary novel contributions in this research work are 1) a shape constrained deformable model formulation to match a generic model to shape information in multiple view silhouettes in the presence of visual ambiguities; and 2) a model-based multiple view reconstruction algorithm to recover a model that matches appearance across multiple views to sub-pixel accuracy. Model-based multiple view reconstruction of people is evaluated and results are presented for the reconstruction of shape and appearance of people in an arbitrary pose. The recovered models provide an accurate shape representation for a person and a visual appearance approaching the quality of the original camera images. The models also provide a consistent structured representation for the editing, synthesis and transmission of 3D content in computer graphics and animation.

Image registration is the automatic alignment of images. It is a fundamental task in computer vision. Image registration is challenging, in part, because of the wide range of applications with an equally wide range of content. Applications that require the automatic alignment of images include: super-resolution, face detection, video coding, medical imaging, mosaicking, post-production video effects, and satellite image registration. The wide and diverse range of applications have led to a wide and diverse range of image registration algorithms. An image registration algorithm is defined by its transformation, criterion, and search. The transformation is the model of image deformation required for alignment. The criterion is the definition of the best registration. The search describes how the best registration is to be found. This thesis presents two image registration methods; fast robust correlation and orientation correlation. The presented methods find translational transformations. Both define their criterion of the best registration using robust statistics. Fast robust correlation applies robust statistics to pixel intensity differences. Orientation correlation applies robust statistics to differences in orientation of intensity gradient. This gives orientation correlation the property of illumination invariance. Both use an exhaustive search to find the best registration. The novelty of fast robust correlation and orientation correlation is the combination of robust statistics, with an exhaustive search that can be computed quickly with fast Fourier transforms (FFTs). This is achieved by expressing a statistically robust registration surface with correlations. The correlations are computed quickly using FFTs. Computation with FFTs is shown to be particularly advantageous in registration of large images of similar size. Experimental comparisons demonstrate the advantages of the methods over standard correlation-based approaches. Advantage is shown in the experiments of: video coding, video frame registration, tolerance of rotation and zoom, registration of multimodal microscopy images, and face registration.

No description available.

An adaptive test is one in which the number of test items and the order in which the items are presented are computed during the delivery of the test so as to obtain an accurate estimate of a student's knowledge, with a minimum number of test items. This thesis is concerned with the design and development of computerised adaptive tests for use within educational settings. Just as, in the same setting, intelligent tutoring systems are designed to emulate human tutors, adaptive testing systems can be designed to mimic effective informal examiners. The thesis focuses on the role of adaptive testing in student modelling, and demonstrates the practicality of constructing such tests using expert emulation. The thesis makes the case that, for small scale adaptive tests, a construction process based on the knowledge acquisition technique of expert systems is practical and economical. Several experiments in knowledge acquisition for the construction of an adaptive test are described, in particular, experiments to elicit information for the domain knowledge, the student model and the problem progression strategy. It shows how a description of a particular problem domain may be captured using traditional techniques that are supported by software development in the constraint logic extension to Prolog. It also discusses knowledge acquisition techniques for determining the sequence in which questions should be asked. A student modelling architecture called SKATE is presented. This incorporates an adaptive testing strategy called XP, which was elicted from a human expert. The strategy, XP, is evaluated using simulations of students. This approach to evaluation facilitates comparisons between approaches to testing and is potentially useful in tuning adaptive tests.

My experience teaching Natural Language Processing (NLP) methods with biblical sources is quite varied. I have taught both novice and advanced students in full semester courses, week-long summer school sessions, and even shorter eight or sixteen hour block sessions. I have also taught students in both the humanities and in computer science. I will thus organize the following article as a report of these experiences focusing especially on the things that I have done that I believe have worked well and those which I think did not worked so well. I should preface all of these remarks also by saying that the methods I use for teaching NLP are only one way to do it. I have had good results using them and I believe that they work, but I also believe that there are other pedagogical methods that could work equally well for a different instructor in a different context.

Truckage companies need continuous and up-to-date information about their business processes in order to respond quickly to customers’ needs and problems emerging during transport processes. Therefore a reliable and user-friendly communication system is required, which improves the relationship between drivers and dispatchers. The project ”Mobile Spedition im Web (SpiW)**” presented here, develops a mobile communication system, which focuses on the driver/dispatcher interaction. The main goals are integration with legacy logistics software and the possible use of new telematics and communication techniques. To achieve these goals, a component based architecture allows the later change and extension of components, making it possible to add new features to the system as they become available. A distributed workflow server supports the adjustment of business processes to individual needs.

The Prosopographic Database of German Engineers 1825–1970 contains a multitude of biographical information. Given a set of research interests by collaborating historians, this paper discusses the steps undertaken (1) to extract engineering subjects from unstructured text entries in the database accompanied with geospatial and temporal information, (2) to adapt existing visual representations to facilitate exploratory analyses, and (3) to design a visual interface to support the interactive composition of engineering branches from engineering subjects to enable the comparative analysis of geospatial-temporal developments in engineering. Usage scenarios outline the benefit of the proposed visualizations for modern prosopography research.

Various time-based visualization techniques have been designed to support the temporal analysis of data collections. While quantities play a secondary role in traditional timelines that reserve space for each individual data item to be observed, time graphs rather display quantitative information and they provide interaction means to filter for a subset of the data. Timages is a hybrid approach that enhances quantitative time graphs with qualitative information in an infographic-style. By (1) scaling thumbnails of data items dependent on relevance to the observed topic and by (2) time-dependent positioning these thumbnails inside a temporally aligned area with a novel space-filling strategy, the most relevant items in the entire data collection as well as predominant data items of certain time ranges are instantly seizable without the need to interact with the time graph.

Public deliberations are organised by governments and other large institutions to take the views of citizens around controversial issues. Increasing public demand and the associated burden on public funding make the quality of public deliberation events and their outcomes critical to modern democracies. This paper focuses on technology developed around streams of computational argument data intended to inform and improve deliberative communication in real time. Combining state-of-the-art speech recognition, argument mining, and analytics, we produce dynamic, interactive visualisations intended for non-experts, deployed incrementally in real time to deliberation participants via large screens, hand-held and personal computing devices. The goal is to bridge the gap between theoretical criteria on deliberation quality from the political sciences and objective analytics calculated automatically from computable argument data in actual public deliberations, presented as a set of visualisations which work on stream data and are simple, yet informative enough to make a positive impact on deliberative outcomes.

SuccessVis is a flexible web-based visual analytics system for presenting and evaluating academic impact. The system is developed for stakeholders who have little training in visualization techniques and have invested in academic programs, specifically in the VACCINE (Visual Analytics for Command, Control and Interoperability Environments) Center, to understand the impact of the center and, thus, better evaluate their future investments in academic research centers. The system is capable of displaying metrics indicating the total impact of the academic research center and allows comparisons between the impact of individual projects, and the dynamics and evolvement of the impact. In this paper, we present the design process of the system, the final product displaying the impact of the VACCINE Center over the past eight years, and discuss the challenges in visualizing academic impact.