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Im Bereich Augmented Reality ist es von großer Bedeutung, dass virtuelle
Objekte möglichst realistisch in ein Kamerabild eingebettet werden. Nur
so ist es möglich, dem Nutzer eine immersive Erfahrung zu bieten. Dazu
gehört unter anderem, Verdeckung dieser Objekte korrekt zu behandeln.
Während schon verschiedene Ansätze existieren, dieses Verdeckungsproblem
zu beheben, wird in dieser Arbeit eine Lösung mittels Natural Image
Matting vorgestellt. Mit Hilfe einer Tiefenkamera wird das Kamerabild in
Vorder- und Hintergrund aufgeteilt und anschließend das virtuelle Objekt
im Bild platziert. Für Bereiche, in denen die Zugehörigkeit zu Vorder- oder
Hintergrund nicht eindeutig ist, wird anhand bekannter Pixel ein Transparenz-
Wert geschätzt. Es werden Methoden präsentiert, welche einen
Ablauf des Image Matting in Echtzeit ermöglichen. Zudem werden
Verbesserungsmöglichkeiten dieser Methoden präsentiert und gezeigt, dass
durch diese eine höhere Bildqualität für schwierige Szenen erreicht wird.
One of the greatest goals in computer graphics is the aesthetic representation of objects. In addition to conventional methods, another field focuses on non-photorealistic renderings. The so-called example-based rendering is an area where users can transfer their art style to a pre-computed 3D rendering, using a hand-painted template. There are some algorithms that already provide impressive results, but their problem is that most of these procedures count as offline methods and are not able to produce results in real-time. For this reason, this work show a method that satisfies this condition. In addition, the influence of the run-time reduction on the results is investigated. Requirements are defined, to which the method and its results are examined. Other methods in this field are referenced and compared with their results.
In scientific data visualization huge amounts of data are generated, which implies the task of analyzing these in an efficient way. This includes the reliable detection of important parts and a low expenditure of time and effort. This is especially important for the big-sized seismic volume datasets, that are required for the exploration of oil and gas deposits. Since the generated data is complex and a manual analysis is very time-intensive, a semi-automatic approach could on one hand reduce the time required for the analysis and on the other hand offer more flexibility, than a fully automatic approach.
This master's thesis introduces an algorithm, which is capable of locating regions of interest in seismic volume data automatically by detecting anomalies in local histograms. Furthermore the results are visualized and a variety of tools for the exploration and interpretation of the detected regions are developed. The approach is evaluated by experiments with synthetic data and in interviews with domain experts on the basis of real-world data. Conclusively further improvements to integrate the algorithm into the seismic interpretation workflow are suggested.
Using semantic data from general-purpose programming languages does not provide the unified experience one would want for such an application. Static error checking is lacking, especially with regards to static typing of the data. Based on the previous work of λ-DL, which integrates semantic queries and concepts as types into a typed λ-calculus, this work takes its ideas a step further to meld them into a real-world programming language. This thesis explores how λ-DL's features can be extended and integrated into an existing language, researches an appropriate extension mechanism and produces Semantics4J, a JastAdd-based Java language semantic data extension for type-safe OWL programming, together with examples of its usage.
Motion capture refers to the process of capturing, processing and trans- lating real motions onto a 3D model. Not only in the movie and gaming industries, motion capture creates an indispensable realism of human and animal movement. Also in the context of robotics, medical movement therapy, as well as in AR and VR, motion capture is used extensively. In addition to the well established optical processes, especially in the last three areas, alternative systems based on inertial navigation (IMUs) are being used in-creasingly, because they do not rely on external cameras and thus limit the area of movement considerably less.
Fast evolving technical progress in the manufacturing of such IMUs allows building small sensors, wearable on the body which can transfer movements to a computer. The development of applying inertial systems to a motion capture context, however, is still at an early state. Problems like drift can currently only be minimized by adding additional hardware for correcting the read data.
In the following master thesis an IMU based motion capture system is designed and constructed. This contains the assembly of the hardware components as well as processing of the received movement data on the software side and their application to a 3D model.
This thesis explores the possibilities of probabilistic process modelling for the Computer Supported Cooperative Work (CSCW) systems in order to predict the behaviour of the users present in the CSCW system. Toward this objective applicability, advantages, limitations and challenges of probabilistic modelling are excavated in context of CSCW systems. Finally, as a primary goal seven models are created and examined to show the feasibilities of probabilistic process discovery and predictions of the users behaviour in CSCW systems.
This thesis proposes the use of MSR (Mining Software Repositories) techniques to identify software developers with exclusive expertise about specific APIs and programming domains in software repositories. A pilot Tool for finding such
“Islands of Knowledge” in Node.js projects is presented and applied in a case study to the 180 most popular npm packages. It is found that on average each package has 2.3 Islands of Knowledge, which is possibly explained by the finding that npm packages tend to have only one main contributor. In a survey, the maintainers of 50 packages are contacted and asked for opinions on the results produced by the Tool. Together with their responses, this thesis reports on experiences made with the pilot Tool and how future iterations could produce even more accurate statements about programming expertise distribution in developer teams.
Mapping ORM to TGraph
(2017)
Object Role Modeling (ORM) is a semantic modeling language used to describe objects and their relations amongst each other. Both objects and relations may be subject to rules or ORM constraints.
TGraphs are ordered, attributed, typed and directed graphs. The type of a TGraph and its components, the edges and vertices, is defined using the schema language graph UML (grUML), a profiled version of UML class diagrams. The goal of this thesis is to map ORM schemas to grUML schemas in order to be able to represent ORM schema instances as TGraphs.
Up to this point, the preferred representation for ORM schema instances is in form of relational tables. Though mappings from ORM schemas to relational schemas exist, those publicly available do not support most of the constraints ORM has to offer.
Constraints can be added to grUML schemas using the TGraph query language GReQL, which can efficiently check whether a TGraph validates the constraint or not. The graph library JGraLab provides efficient implementations of TGraphs and their query language GReQL and supports the generation of grUML schemas.
The first goal of this work is to perform a complete mapping from ORM schemas to grUML schemas, using GReQL to sepcify constraints. The second goal is to represent ORM instances in form of TGraphs.
This work gives an overview of ORM, TGraphs, grUML and GReQL and the theoretical mapping from ORM schemas to grUML schemas. It also describes the implementation of this mapping, deals with the representation of ORM schema instances as TGraphs and the question how grUML constraints can be validated.
The extensive literature in the data visualization field indicates that the process of creating efficient data visualizations requires the data designer to have a large set of skills from different fields (such as computer science, user experience, and business expertise). However, there is a lack of guidance about the visualization process itself. This thesis aims to investigate the different processes for creating data visualizations and develop an integrated framework to guide the process of creating data visualizations that enable the user to create more useful and usable data visualizations. Firstly, existing frameworks in the literature will be identified, analyzed and compared. During this analysis, eight views of the visualization process are developed. These views represent the set of activities which should be done in the visualization process. Then, a preliminary integrated framework is developed based on an analysis of these findings. This new integrated framework is tested in the field of Social Collaboration Analytics on an example from the UniConnect platform. Lastly, the integrated framework is refined and improved based on the results of testing with the help of diagrams, visualizations and textual description. The results show that the visualization process is not a waterfall type. It is the iterative methodology with the certain phases of work, demonstrating how to address the eight views with different levels of stakeholder involvement. The findings are the basis for a visualization process which can be used in future work to develop the fully functional methodology.
The Internet of Things (IoT) recently developed from the far-away vision of ubiquitous computing into very tangible endeavors in politics and economy, implemented in expensive preparedness programs. Experts predict considerable changes in business models that need to be addressed by organizations in order to respond to competition. Although there is a need to develop strategies for upcoming transformations, organizational change literature did not turn to the specific change related to the new technology yet. This work aims at investigating IoT-related organizational change by identifying and classifying different change types. It therefore combines the methodological approach of grounded theory with a discussion and classification of identified change informed by a structured literature review of organizational change literature. This includes a meta-analysis of case studies using a qualitative, exploratory coding approach to identify categories of organizational change related to the introduction of IoT. Furthermore a comparison of the identified categories to former technology-related change is provided using the example of Electronic Business (e-business), Enterprise Resource Planning (ERP) systems, and Customer Relationship Management (CRM) systems. As a main result, this work develops a comprehensive model of IoT-related business change. The model presents two main themes of change indicating that personal smart things will transform businesses by means of using more personal devices, suggesting and scheduling actions of their users, and trying to avoid hazards. At the same time, the availability of information in organizations will further increase to a state where information is available ubiquitously. This will ultimately enable accessing real time information about objects and persons anytime and from any place. As a secondary result, this work gives an overview on concepts of technology-related organizational change in academic literature.