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Data visualization is an effective way to explore data. It helps people to get a valuable insight of the data by placing it in a visual context. However, choosing a good chart without prior knowledge in the area is not a trivial job. Users have to manually explore all possible visualizations and decide upon ones that reflect relevant and desired trend in the data, are insightful and easy to decode, have a clear focus and appealing appearance. To address these challenges we developed a Tool for Automatic Generation of Good viSualizations using Scoring (TAG²S²). The approach tackles the problem of identifying an appropriate metric for judging visualizations as good or bad. It consists of two modules: visualization detection: given a data-set it creates a list of combination of data attributes for scoring and visualization ranking: scores each chart and decides which ones are good or bad. For the later, an utility metric of ten criteria was developed and each visualization detected in the first module is evaluated on these criteria. Only those visualizations that received enough scores are then presented to the user. Additionally to these data parameters, the tool considers user perception regarding the choice of visual encoding when selecting a visualization. To evaluate the utility of the metric and the importance of each criteria, test cases were developed, executed and the results presented.
Stylized image triangulation
(2019)
Stylized image triangulation is a popular tool of image processing. Results can be found on magazine covers or bought as a piece of art. Common use cases are filters by mobile apps or programs dedicated to automated triangulation. This thesis is based upon a paper that achieves new results formulating the adaptive dynamic triangulation as optimization problem. With this approach, new results concerning visual and technical quality are accomplished. One aim of this thesis is to make this approach accessible to as many users as possible. To reach users, a mobile app called Mesh is designed and implemented. A client-host-system is presented which relieves the app from computing the result requiring a lot of resources. Therefore, transferring the approach to a CPU based solution is part of the thesis. Also, a webserver is implemented that handles the communication between app and algorithm. “Mesh” enables the user to send a arbitrary image to the server whose result can be downloaded.
Part of the research deals with optimizing the method. As the main step, the gradient descent method, which minimizes an approximation error, is examined with three different approaches re-defining the movement of a point: The limitation of the directions of movement in a meaningful manner, diagonal directions and a dynamically repositioning of points are analyzed. Results show no improvement of visual quality using diagonal instead of horizontal and vertical steps. Disallowing a point to take its last position, the limitation of step opportunities results in a loss of visual quality but reaches an intended global error earlier. The dynamically repositioning rests upon a vectorbased solution that weights the directions and applies a factor to each of them. This results in a longer computational time but also in a higher visual quality.
Inspired by the work of Josh Bryan, another part of research aims at imitating an artists style. With the use of pseudo-random events combined with a geometryshader, a more natural look shall be achieved. This method illustrates a way of adding minor details to a rendering. To imitate an artist's work, a more complex and more precise triangulation is needed. As the last aspect, a renderstyle is presented. The style uses a center for its effect moving the triangles of a triangulation apart. The arbitrary choice of placing the centrum enables the renderstyle to be used in animations.
Bio-medical data comes in various shapes and with different representations.
Domain experts use such data for analysis or diagnosis,
during research or clinical applications. As the opportunities to obtain
or to simulate bio-medical data become more complex and productive,
the experts face the problem of data overflow. Providing a
reduced, uncluttered representation of data, that maintains the data’s
features of interest falls into the area of Data Abstraction. Via abstraction,
undesired features are filtered out to give space - concerning the
cognitive and visual load of the viewer - to more interesting features,
which are therefore accentuated. To address this challenge, the dissertation
at hand will investigate methods that deal with Data Abstraction
in the fields of liver vasculature, molecular and cardiac visualization.
Advanced visualization techniques will be applied for this purpose.
This usually requires some pre-processing of the data, which will also
be covered by this work. Data Abstraction itself can be implemented
in various ways. The morphology of a surface may be maintained,
while abstracting its visual cues. Alternatively, the morphology may
be changed to a more comprehensive and tangible representation.
Further, spatial or temporal dimensions of a complex data set may
be projected to a lower space in order to facilitate processing of the
data. This thesis will tackle these challenges and therefore provide an
overview of Data Abstraction in the bio-medical field, and associated
challenges, opportunities and solutions.
This work describes a novel software tool for visualizing anatomical segmentations of medical images. It was developed as part of a bachelor's thesis project, with a view to supporting research into automatic anatomical brain image segmentation. The tool builds on a widely-used visualization approach for 3D image volumes, where sections in orthogonal directions are rendered on screen as 2D images. It implements novel display modes that solve common problems with conventional viewer programs. In particular, it features a double-contour display mode to aid the user's spatial orientation in the image, as well as modes for comparing two competing segmentation labels pertaining to one and the same anatomical region. The tool was developed as an extension to an existing open-source software suite for medical image processing. The visualization modes are, however, suitable for implementation in the context of other viewer programs that follow a similar rendering approach.
The modified code can be found here: soundray.org/mm-segmentation-visualization.tar.gz.
Social media provides a powerful way for people to share opinions and sentiments about a specific topic, allowing others to benefit from these thoughts and feelings. This procedure generates a huge amount of unstructured data, such as texts, images, and references that are constantly increasing through daily comments to related discussions. However, the vast amount of unstructured data presents risks to the information-extraction process, and so decision making becomes highly challenging. This is because data overload may cause the loss of useful data due to its inappropriate presentation and its accumulation. To this extent, this thesis contributed to the field of analyzing and detecting feelings in images and texts. And that by extracting the feelings and opinions hidden in a huge collection of image data and texts on social networks After that, these feelings are classified into positive, negative, or neutral, according to the features of the classified data. The process of extracting these feelings greatly helps in decision-making processes on various topics as will be explained in the first chapter of the thesis. A system has been built that can classify the feelings inherent in the images and texts on social media sites, such as people’s opinions about products and companies, personal posts, and general messages. This thesis begins by introducing a new method of reducing the dimension of text data based on data-mining approaches and then examines the sentiment based on neural and deep neural network classification algorithms. Subsequently, in contrast to sentiment analysis research in text datasets, we examine sentiment expression and polarity classification within and across image datasets by building deep neural networks based on the attention mechanism.
