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"title": "Analysis of the GPU Acceleration Potential of the FFT-Based Pressure Solver in the PALM-4U Model System",
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"abstract": "Due to climate change, the frequency and severity of extreme weather events is increasing, which endangers human livelihoods and key infrastructures. Decision support tools can guide the development of climate-resilient cities by providing information on the potential effectiveness of specific measures during the planning process. In urban environments,decision support tools that incorporate accurate micro-climate models are particularly effective. PALM-4U, a state-of-the-art, scientifically validated microclimate model, could offer this functionality, however it remains largely inaccessible outside the scientific community as it is optimised to run on HPC clusters. However, with the rise of high-performance GPUs, a shift towards single workstations is possible.This study investigates the potential for performance increase of the PALM-4U’s pressuresolver, by utilising the GPU’s acceleration potential in combination with a change intarget architecture. Performance increase is measured using three parameters: speed up,validity (via NMSE, R, and FB), and memory efficiency. Also the effect on the runtime of the full simulation is measured and possible bottlenecks identified. Finally, the fullmodel is analysed to assess the overall feasibility of GPU optimisation, providing insights to guide future development.The pressure solver transforms the 3D Poisson equation using Fast Fourier Transform and solves the resulting 1D system via the Thomas algorithm. The code structure is optimised, CUDA-optimised kernels are implemented and the cuFFT library is integrated.In addition a mixed-precision approach is tested to evaluate its impact on performance and accuracy.The single core GPU implementation achieves a speed up of up to 65.5 times in single precision and up to 49.3 times for double precision for large domain sizes. The stability of the system remains unaffected by the mixed-precision approach, and no significant variation is observed between FP32 and FP64 runs. After 45 × 103 simulation steps, NMSE (0.02), FB (-0.017) and R (0.96), demonstrate a stable and accurate performance consistent across precisions. Additionally, the memory requirement is reduced up to 68% compared to the baseline CPU solver. The optimisations leads to a runtime reduction ofthe full model by 15%, demonstrating the potential for accessible, scientifically validated microclimate models.",
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"abstract": "We designed a highly flexible notebook-based visual analysis pipeline to explore Transcranial Magnetic Stimulation (TMS) and heart rate (HR) in different subjects’ cognitive states. TMS is a promising treatment of major depressive disorder not responsive to pharmacological treatment. However, the mechanism of action is not yet fully understood. The research in the best acquiring settings, such as stimulation intensities and target sites, is emerging. Multimodal analysis pipeline integrating TMS, Functional Magnetic Resonance Imaging (fMRI), and HR could shed light on both understanding the neural pathways and increasing the efficiency of TMS. To extend the already available concurrent TMS-fMRI analysis pipeline towards multimodal concurrent TMS-fMRI-HR, exploring the effect of TMS on HR is the next step. To design the visual analysis pipeline, we introduce and apply an extension of Data–Users–Tasks design triangle [Miksch and Aigner, 2014] by integrating the previous data workflow approach in the designing process. When the data processing workflow in the domain is only evolving, integrating the previous workflow approach into the design process benefits by respecting the data legacy, supports users’ adaptability, and ensures tasks’ compatibility. We refer to this framework as the Data–Users–Workflow-Tasks design pyramid. We subsequently provide a visual analysis pipeline to support data exploration in the early stages of research. The interactive preprocessing pipeline involves extracting data, handling missing data, and reducing noise. To compare time series of HR with different properties, we visualize Dynamic Time Warping (DTW) similarity measurement, and heart rate variability (HRV) metric clustering. We quantitatively evaluate the preprocessing steps using simulated ECG data. The key result is that linear and polynomial interpolation with root mean squared error (RMSE) values as low as to the power of -3 and -5, respectively, are especially effective as imputation methods for ECG with 400 Hz sampling frequency. To further assess the values of the usage scenarios for TMS and ECG data exploration, we employ Qualitative Result Inspection (QRI). Our proposed visual analysis pipeline assembles the first steps towards integrating TMS-HR analysis into a trimodal concurrent TMS-fMRI-HR approach.",
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"title": "Unraveling uncertainty propagation in the medical visualization pipeline",
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"abstract": "Quantifying, raising awareness, and visualizing uncertainty stand as challenges in data visualization, especially in critical application domains such as medicine. Medical diagnosis and following treatment are always based on human decision-making, which itself is prone to uncertainty due to subjectivity or perception. Furthermore, decisions are often taken by analyzing measurements or images, which themselves are affected by uncertainty, caused by effects such as noise or resolution limitations. Thus, the whole process of diagnosis in clinical environments is concerned with interwoven uncertainties that accumulate and may change a pipeline's result substantially, potentially with detrimental effects on the patient's health, if uncertainties are not considered. This work aims to contribute to unraveling the complex interplay of uncertainties within the medical visualization pipeline. We do so by investigating the complex phenomena of uncertainty propagation in the medical visualization pipeline, in combination with extracting and analyzing provenance information from the pipeline encapsulated in an interactive framework. As a consequence, we utilize the provenance information, which can be seen as a complete history of the pipeline, to compare uncertainty propagation results of distinct pipeline states and thus gain insights into the behavior of uncertainty. In order to demonstrate the conceptual effectiveness of the framework, meaningful usage scenarios are presented. Those lay out simple and more complex scenarios to analyze the behavior and impact of different sorts of parameters present in the pipeline. Furthermore, we present ways in which a user can express their uncertainty for certain image regions or parameters and thereby gain insights into the impact of the specified uncertainties. The usage scenarios emphasize both positive and negative aspects of the framework and thus provide users with the means to assess the underlying work independently.",
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"title": "Flattening-based visualization of supine breast MRI",
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"abstract": "We propose two novel visualization methods optimized for supine breast images that “flatten” breast tissue, facilitating examination of larger tissue areas within each coronal slice. Breast cancer is the most frequently diagnosed cancer in women, and early lesion detection is crucial for reducing mortality. Supine breast magnetic resonance imaging (MRI) enables better lesion localization for image-guided interventions; however, traditional axial visualization is suboptimal because the tissue spreads over the chest wall, resulting in numerous fragmented slices that radiologists must scroll through during standard interpretation. Using a human-centered design approach, we incorporated user and expert feedback throughout the co-design and evaluation stages of our flattening methods. Our first proposed method, a surface-cutting approach, generates offset surfaces and flattens them independently using As-Rigid-As-Possible (ARAP) surface mesh parameterization. The second method uses a landmark-based warp to flatten the entire breast volume at once. While the surface-cutting approach is based on the parameterization of individual surfaces, the second method uses control points to realize a coherent, global deformation.Expert evaluations revealed that the surface-cutting method provides intuitive overviews and clear vascular detail, with low metric (2.1-3.5%) and area (3.7-5.8%) distortions. However, independent slice flattening can introduce depth distortions across layers. The landmark warp offers consistent slice alignment and supports direct annotations and measurements, with radiologists favoring it for its anatomical accuracy. Both methods significantly reduced the number of slices needed to review, highlighting their potential for time savings and clinical impact - an essential factor for adopting supine breast MRI.",
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"title": "Evaluating the impact of parameter tuning on glioblastoma segmentation using deep learning",
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"abstract": "Background: Glioblastoma multiforme (GBM) is the most aggressive form of brain cancer, characterized by rapid growth and infiltration into surrounding brain tissue. Precise segmentation of GBM, particularly the contrast-enhancing region and necrotic (non-contrast-enhaning) core, is critical for surgical planning and treatment. Manual segmentation methods are time-consuming and subject to high interrater variability, necessitating automated approaches for greater consistency.Objective: This thesis aims to optimize key parameters in deep learning-based segmentation of glioblastomas, focusing on the impact of Batch size, data augmentation strategies, and the number of training cases on model performance, along with tuning the Focal Weight Factor in the Combined Loss Function. The goal is to improve the accuracy of segmenting clinically relevant tumor regions.Methods: In this study, 3D U-Net models were trained using the BraTS Challenge dataset, which includes multimodal MRI scans (T1 post-contrast, FLAIR, and T2) with expert-labeled segmentations reviewed by a neuroradiologist to eliminate interrater variability. The models were evaluated on 108 unseen clinical cases from patients at the University Hospital Salzburg to assess their generalization capability and performance. Segmentation accuracy was measured using Intersection over Union (IoU) and a Custom Weighted Dice Score, focusing on Dice coefficients for the contrast-enhancing and non-contrast-enhancing tumor. Four Case Groups (80, 160, 240, and 314) were used to examine the effect of Case Group size on performance.Results: Models trained with Batch size of four consistently ranked among the top performers, with 80% making it into the top 10, suggesting that larger Batch sizes contribute to better generalization and stability as number of training cases increase. However, augmentations generally resulted in worse performance, except for one outlier—the best performing model—trained with a 1:1 ratio of augmentations to originals, Case Group 314, and a Batch size of one, which performed exceptionally well.Conclusion: Augmentations with a ratio of 1:3 performed poorly, particularly when three variants of one original were included in a Batch size of four, leading to overfitting. This suggests a lack of diversity within the batches caused the model to overfit, whereas a strategy mixing different augmentations within each batch led to better generalization. Case Group 314 models performed best, highlighting the importance of more training data for improved performance.",
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"title": "Cycle Safely",
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"abstract": "This thesis presents a design and implementation of an end-to-end collision prediction and detection pipeline tailored to cyclists. The primary goal is to predict potential collisions between the cyclist (ego agent) and other road users, particularly motorised vehicles, which pose a higher risk due to higher momentum and speed. The proposed pipeline integrates conventional techniques for solving the sub-tasks of object detection, object tracking, and trajectory forecasting. Specifically, Super Fast and Accurate 3D Object Detection (SFA3D) is used for the detection, a Kalman filter-based multi-object tracker for temporal association of these detections, and a machine learning-based model (prediction conditioned on goals in visual multi-agent settings) is adapted and trained for the prediction of future trajectories. CARLA driving simulator facilitates the training and development of the prediction model by creating a synthetic dataset of cycling and the interaction with other road users. The system is evaluated on the synthetic dataset and also on the real-world KITTI dataset, and additional ablation studies examine the contribution of each pipeline stage. Experiments demonstrate that the proposed approach is capable of achieving reliable performance in object detection and tracking tasks. This confirms the feasibility of such a pipeline under limited sensing capabilities, such as LIDAR and GPS/IMU measurements. However, trajectory prediction remains a difficult and computationally expensive task, primarily due to the lack of documented and easily deployable open-source models. The implementation comes with a visualisation framework, built from the Rerun tool, for interactive inspection of the pipeline’s intermediate and final results.The contribution of this thesis can be summarised by the implementation of a framework for cyclist collision detection. It offers insights into how conventional and machine learning methods can be combined into a pipeline, and key limitations and points of future work for the creation of better trajectory prediction in adverse traffic contexts are outlined.",
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"title": "Simulating Windows for Lighting Design Optimization",
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"abstract": "For lighting design, optimizing windows plays a major role, especially for office and living spaces, as natural light is important for focus, productivity and also mood. The rendering framework Tamashii, which is currently in development at the research unit of computer graphics at TU Wien, offers a feature to automatically optimize multiple parameters of light sources like the position, intensity or rotation for a predefined lighting target. This thesis aims to expand the possibilities Tamashii offers for lighting design by simulating windows through area lights. Tamashii’s automatic light parameter optimization relies on light tracing, which unlike path tracing, casts the light rays from the light sources instead of the camera. This is why implementing environment maps in a classical sense is not feasible, as emitting light rays from each pixel of the environment map only for a small percentage to go through the window is very inefficient. We implement a new type of light that combines area lights with Illuminating Engineering Society (IES) lights in order to simulate windows. The IES standard is a file format commonly used by luminaire manufacturers to describe the physical properties of a luminaire for simulation in software. To accurately mimic the light that shines through real windows, we convert High Dynamic Range (HDR) files into IES profiles, which our lights can then use. Our new light type can also be attached to models in the scene, such as walls or roofs, which constrains the manual movement of the windows to the connected object and makes their usage more intuitive. In our tests, we find that our implementation is able to realistically simulate real windows when compared to the same combination of scenes and HDR files in Blender’s path tracing renderer Cycles. To ensure that the light parameter optimization algorithm only moves the window lights inside the model its connected to, we implement a constraint that gets evaluated repeatedly while optimizing. We realize this by calculating penalties when the light reaches the edges of the model, in order to encourage the algorithm to keep the window light inside. When evaluating our implementation we find that with the activated constraint, the algorithm is able to find valid positions for the window lights when optimizing.",
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"abstract": "Traditional machine learning approaches for analyzing large unstructured data often depend on labelled training data and well-defined target definitions. However, these may not be available or feasible when dealing with unknown and unstructured data. It requires human reasoning and domain knowledge to interpret it. Interactive systems that combine human analytical abilities with machine learning techniques can address this limitation. However, to incorporate human knowledge in such systems, we need a better understanding of the semantic information and structures that users observe and expect while exploring unstructured data, as well as how they make their tacit knowledge explicit. This thesis aims to narrow the gap between human cognition and (knowledge-assisted) visual analytics. In a qualitative and exploratory user study, this thesis investigates how individuals explore a large unstructured dataset and which strategies they apply to externalize their mental models. By analyzing users' externalized mental models, we aim to better understand how their knowledge evolves during data exploration. We evaluate the comprehensiveness, detail and evolution of users' external knowledge representations by applying quantitative and qualitative methods, including a crowdsourcing step. The results show that users' externalized structures are able to represent a given dataset comprehensively and to a high degree of detail. While these knowledge representations are highly subjective and show various individual differences, we could identify structural similarities between individuals. In addition to the insights about how users externalize their tacit knowledge during data exploration, we propose design guidelines for (knowledge-assisted) visual analytics systems.",
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"abstract": "Human annotation of image data is relevant for supervised machine learning, where labeled datasets are essential for training models. Traditionally, reducing the labeling effort was achieved through active learning, where the optimal next instance for labeling is selected by some heuristic to maximize utility. More recent work has focused on integrating user initiative in the labeling process through visual interactive labeling to steer the labeling process. This thesis proposes cVIL, a class-centric approach for visual interactive labeling that simplifies the human annotation process for large and complex image datasets. Previously, visual labeling approaches were typically instance-based, where the system visualizes individual instances for the user to label. cVIL utilizes diverse property measures to enable the labeling of difficult instances individually and in batches to label simpler cases rapidly. Since the property measures express the properties of an instance using a single scalar value, the visualizations are simple and scalable. cVIL combines the heuristic guidance approach of active learning with the user-centered approach of visual interactive labeling. In simulations, we could show that property measures can facilitate effective instance and batch labeling. In a user study, cVIL demonstrated superior accuracy and user satisfaction compared to the conventional instance-based visual interactive labeling approach that employs scatterplots. Participants also needed less time to complete the assigned tasks in cVIL compared to the baseline.",
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"abstract": "Many deep learning applications are based on graph data in order to explore relationships or to analyze structures. Labeling this data is expensive and often requires expert knowledge. For the application of graph clustering to neuron data, the SOTA method GraphDINO generates self-supervised graph embeddings combined with the downstream task of clustering these embeddings. We observe on a particularly challenging neuron dataset that this method does not lead to satisfying clustering results. Therefore we use the graph embeddings generated by GraphDINO as an initial starting point to improve the network and to guide the network training. To achieve this, we developed the visual analytics framework NetDive. The user can analyze the graph embeddings and label single neurons that are falsely clustered. This annotation information is then used to train a semi-supervised model. To this end, we developed a network architecture, named GraphPAWS, that assembles components of GraphDINO and of the semi-supervised network architecture PAWS. The model training can be started from within the visual analytics application NetDive and the resulting graph embeddings are available in NetDive as soon as the retraining is completed. We demonstrate how we iteratively train the model using NetDive and GraphPAWS and evaluate our model against the self-supervised SOTA for our dataset.",
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"title": "Automated Extraction of Complexity Measures from Engineering Drawings",
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"abstract": "An engineering drawing is a detailed representation of an object used to communicate complex information for the purposes of design, manufacturing, and maintenance.These line drawings typically consist of multiple 2D orthographic views of a 3D object, along with dimensioning information and metadata about specific properties.Over the past decades, engineering drawings have evolved from hand-drawn sketches to highly standardized documents created with the help of CAD software.The large variety of engineering drawings makes it difficult to automatically extract abstract information in a robust way.The emergence of additive manufacturing (AM) promises companies that they can produce spare parts on demand for maintenance, potentially increasing the operational time of their infrastructure.Evaluating the AM potential of spare parts is essential from both an economic and technical perspective.This analysis of economic and technical viability requires the interpretation of complexity measures that can be derived from the engineering drawing of a spare part.The external dimensions of an object are key complexity measures to facilitate an AM potential analysis.In this thesis, we propose a processing pipeline that automates the extraction of complexity measures from engineering drawings, focusing on the external dimensions of the depicted objects.An in-depth examination of engineering drawings from different eras forms the basis of our methodology.Our pipeline is designed to be adaptable and consists of interpretable stages for specific tasks.We segment important entities in the input drawing to detect candidate dimension lines that are subsequently filtered by a sequence of processing steps.The grid structure of the orthographic views is determined, which allows us to assign axis labels to each view.We run optical character recognition (OCR) on detected dimension numbers and use the results to optimize the ratio between the OCR values and the length of dimension lines in pixels, providing us with a solution that is resilient to errors in the OCR predictions.A prototypical implementation of our pipeline demonstrates its capabilities in handling a large variety of drawings.We conduct a basic quantitative and qualitative evaluation of our methodology.The results confirm the effectiveness of our approach in automatically extracting abstract information from real-world engineering drawings.",
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"title": "Alpine Terrain Relighting",
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"abstract": "Aerial orthophotos together with digital elevation models (DEMs) allow the rendering of 3D representations of the earth, including alpine terrain. These virtual landscapes provide the opportunity to simulate light conditions at different times of the day, aiding in trip planning. However, orthophotos used as texture often contain large shadows stemming from cliffs and rocks, which significantly impact the visual quality of relighted textures. The necessary single-image shadow-removal process presents a crucial problem for the computer vision domain, which also functions as a prerequisite for many other tasks like segmentation and classification. Many promising approaches have already been developed, but unlike previous methods, this study tries to capitalize on the availability of DEMs to enhance the shadow removal process. Shadows in orthophotos are inherently linked to the underlying geospatial topology, and DEMs provide a valuable source of information for mitigating their impact. Therefore, this thesis explores the integration of DEMs into a state-of-the-art deep learning pipeline. DEMs are examined for their role in generating training sets and as supplementary input for a multi-modal network. Notably, 3D geometry derived from DEMs complemented by ray-tracing is used to generate artificial shadows with realistic shapes. Subsequently, an experiment is conducted with the created dataset to empirically test if additional elevation data is beneficial for the performance of the models. Additionally, the model’s ability to generalize from artificial to real shadows was probed. The experiment on virtual shadows showed that providing additional elevation data to the shadow-removal network does yield significantly better results with a medium to large effect size. Initially, all trained models failed to generalize to real shadow data. Downsizing the dataset to a lower level of detail mitigated this problem. Together with an analysis of the output of each network layer, it was concluded that the reason for the subpar real data performance are remaining small-scale shadows in the train set. A visual analysis of the improved models showed noticeable improvements with the generated realistic shadow shapes compared to random ones. Moreover, the utility of additional elevation data as input for the models was demonstrated.",
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"abstract": "As static visualizations like diagrams, maps, charts, or drawings get more and more important in everyday life, it is crucial to find out how helpful they actually are, especially in comparison to text. This diploma thesis outlines if visualizations or texts are processed faster by humans and which representation is better comprehensible. For this purpose, we conducted an exploratory study in which we measured processing times and error rates when interpreting either texts or visualizations. In a pre-study, in which each participant had to describe four visualizations in their own words, we found out which parts of a visualization are deemed most important by people. The focus of the participants was on extrema, as well as on certain other values. Furthermore, the participants often compared different values in order to describe the visualization. The pre-study gave us valuable insights which we used for writing the texts for our study. For our exploratory study, where we wanted to find out more about whether visualizations or text can be interpreted more easily, we used 15 visualizations and texts that contained the same information. Each participant had to work on at least six topics, thereof at least three by using visualizations and three by using texts, and had to answer three questions per topic at the end. The time was measured while the participants worked on the topics.We could see that the participants solved topics by using visualizations on average about 1.3 times faster as compared to when they used texts. This difference was statistically significant. We could not find significant differences between the error rates for topics, when participants used visualizations or texts. When texts were used, we found correlations between text lengths and processing speeds, as well as text lengths and error rates. The content of visualizations or texts did not seem to play a role for processing speed or error rates. However, we found cases in which topics with visualizations were solved more than 50 % faster as compared to topics with texts. Our results provide a solid basis for defining further hypotheses regarding the readability of visualizations compared to text. In this thesis, we present the final hypotheses that emerge from our exploratory study. We consider these to be extremely interesting for visualization research, as there is much evidence that visual information can be processed faster than text. Whereby it is worth mentioning that the actual increase in performance may be much lower than often claimed in the media (e.g. `60,000 times faster than text'). Furthermore, our results indicate that no significant differences can be found between visual information and text with regard to the error rate in answering final questions.",
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"abstract": "In prostate cancer radiotherapy planning, the accurate description of the position and shape of pelvic organs is a crucial part of successful patient treatment. However, the treatment is conducted throughout a long period of time, during which the position and shape of the organs might significantly vary. In addition, the amount of variation tends to differ for each individual. Recent visual analytics publications investigated this by partitioning past patients into clusters with similar variability. Using this as part of a prediction for the organ variability of new patients could improve and further personalize therapy planning. However, the statistical and machine learning methods employed in these works have not been thoroughly and quantitatively evaluated so far and their impact on the final predictions has not been assessed. This thesis focuses on taking a particular implementation of these approaches, proposed by Furmanová et al. [FMCM+21], and quantitatively evaluating the effects of using different alternatives for the employed methods. We focus on two aspects: the effect of using different shape descriptor methods and the impact of modifications in the clustering methods employed. By providing an additional visual analytics framework to visually assess the effect of the aforementioned alternatives, we aim to ensure an effortless and interactive visual interpretation of the impact of various modifications. This is anticipated to support the developers of said predictive algorithms in designing more robust approaches. As a result of our investigation we have highlighted potential issues and improved the initial implementation of the proposed workflow. We conclude that at the current stage of the patient cohort used for the analysis, the selection of appropriate shape description methods should be of main focus, while a notable impact of using different clustering methods is limited to the prediction of the most extreme cases of organ shape variations.",
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"abstract": "Radiogenomics refers to the combined study of imaging-derived features, called radiomics and gene sequencing data, called genomics. Challenges in the analysis of radiogenomic data include the size, heterogeneity, and complexity of the datasets. These challenges make the analysis of the available information space tedious for cancer experts and hinder the exploration and sensemaking of patient information. This is further hampered when additional clinical information needs to be included in the analyses. Visual Analytics (VA) combines automated analysis techniques, such as machine learning or statistics, together with interactive visual interfaces. It allows users to gain insights into complex data and make effective decisions. In the context of radiogenomics analysis with respect to clinical data, VA approaches offer promising directions in tumor profiling. However, VA approaches that bridge radiogenomic and clinical data in an interactive and flexible visual framework have not been investigated before. In this work, we enable the integrated exploration and analysis of radiogenomic data and clinical information for knowledge discovery and hypothesis assessment in a large cohort of prostate cancer patients. We handle missingness in the data through imputation techniques and apply unsupervised machine learning for the dimensionality reduction and clustering of the data to facilitate data handling and visualization. As a result, we present an interactive visual interface for two target audiences: cancer experts and biomedical data scientists. Our framework enables cancer experts to gain insights into the data by revealing new patterns or correlations in the datasets. It allows them to interactively assess and refine any hypothesis in mind for the underlying datasets. For biomedical data scientists, our framework offers the possibility to understand the analysis components and interactively explore their impact on the outcome. We evaluate the unsupervised machine learning models through similarity measures such as the silhouette coefficient. To assess the usability of the framework, we perform usage scenarios that we confirm by our cancer experts. The feedback from our domain experts reveals that our framework is a suitable and flexible technique to gain insights into large and heterogenous radiogenomic data with respect to clinical data. It promotes knowledge discovery as well as hypothesis creation, assessment, and refinement. Interacting with the different visualization and analysis components enhances the understanding of the data and the resulting visual representations. Our approach incorporates the integration of interactive visualization and automated analysis components. It supports our collaborating domain experts at the Medical University of Vienna to obtain new insights into their data, while investigating hypotheses at hand.",
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"abstract": "Although the concept behind the Game of Life cellular automaton has been explored\ndeeply in the past, it is still difficult to find proper resources on this topic that would\nallow those with little to no experience in the field of IT to properly understand it. No\nsingular solution exists that explains the algorithm behind the automaton, lets the user\ninteract with the simulation, and provides explanations regarding the most common\nbuilding blocks of Life, all in one place. The goal of this bachelor thesis is to create\nan online learning tool, that would enable people with little computer knowledge, to\nengage with the automaton. This is done by offering a range of interactive lessons on\nthe Game of Life, together with a high degree of customizability of the simulation itself.\nAn additional goal of this project is to include a 3D interpretation of the Game of Life\nautomaton, adjusting the algorithm accordingly, allowing users to experiment with the\nadditional third dimension.\nThe Game of Life not being a fresh topic, related work has been researched and compared,\nand the results are laid out below. In addition to the developed web application, this\nthesis goes deeper in exploring the concepts of a Turing machine and Turing complete\nsystems, such as the Game of Life itself. Implementation details are presented, as well as\nideas for the future development of the so-called “Tour of Life”, and its potential features.\nThe results of this work are publicly available, the link to the Tour of Life website can be\nfound at the end of this thesis",
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Serializer Support:
\nLoading resources such as large 3D models or ORCA scenes can take a long time when using ASSIMP to parse the files on every program start. The serializer saves the parsed data in a binary cache file, which is used on subsequent program executions to greatly reduce loading times.
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"title": "Concept Map Mining as Browser Extension",
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"abstract": "Concept maps are a well-known method of structured representation of knowledge. They\nare represented as a node-link diagram that showcases different concepts and their\nrelations to each other, which are often extracted from unstructured text. Manual\ngeneration of such concept maps can be a tedious task, but fully automated approaches\nare often not able to satisfy qualitative expectations. Semi-automated methods have\nshown to be a satisfying compromise between these two. It is especially important that\nthe manual aspect of creating a concept map is as intuitive and easy as possible so that\nthe user’s workflow is not interrupted, and tasks can be completed efficiently. Therefore,\nit is the graphical user interface that plays a critical role in guaranteeing a satisfying\nexperience and swift completion of tasks.\n\nIt is therefore the aim of this thesis to create an environment, in which users are able\nto extract meaningful concepts from arbitrary websites and connect these concepts to\nexisting knowledge structures. In other words, it should visually convey how new, unseen\ninformation fits to the knowledge they already have. For this purpose, an extension to\nthe Google Chrome browser is presented in this thesis, that allows the user to analyze\nthe text on any website on the internet with a provided natural language processing\nsoftware. Concepts and relations can then be highlighted in the original text to visualize\ntheir connection to existing knowledge. At the user’s choice, new concepts, relations, and\ncombinations of the two can be added to existing concept maps. These concept maps\ncan be automatically visualized as a node-link diagram.\n\nWith the help of a small user evaluation, we conclude that the approach has definite\npotential but still lacks the reliability, especially with the automatic text processing and\nconcept extraction, for real-world use-cases.",
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"abstract": "The control of legged robots and teaching robotic hands to grasp are still challenging tasks. Machine learning approaches already work well in simulation. However, the discrepancy between simulation and reality sometimes causes diÿculties when applying simulation results to the real robot. Learning algorithms also require a huge amount of training data. The goal of this work is to build a sandbox that provides a detailed comparison between simulated and real-world robots and o˙ers a way of controlled and continuous data collection and exploration.\nThe sandbox consists of a motion capture and a simulation component. The motion capture component is responsible for the continuous data collection and is realized with a system from OptiTrack with six high-precision infrared cameras. The simulation com-ponent is realized with Simulink and the Simscape Multibody Library. This component is responsible for the exploration and comparison of simulated data with real-world data. The robot that is selected for this work is a small four-legged puppy robot from ROBOTIS that is actuated with 15 Dynamixel servomotors. To integrate the robot into the sandbox, the robot’s controller is reprogrammed to make a transfer from motion data to the robot easier and to control the robot remotely. The robot is programmed with a straight walking gait and equipped with reflective markers to track its movements.\nWith the computer-aided design (CAD) software SolidWorks, a 3D model of the puppy robot is constructed that is used for simulation in Simulink.\nThe result is a system that accurately gathers 6 degrees of freedom (DOF) data of a small robot. This data is transferred to the simulation and can be compared to simulated data. Data from the simulation can also be tested easily on the real robot and tracked again. This way, a closed-loop system is provided for iterative robot exploration.\nTwo datasets are compared with the help of the resulting sandbox: A dataset from ROBOTIS containing ideal joint angles for the robot, and a dataset that is obtained with the motion capture system, containing tracked joint angles. The datasets are simulated and the position and orientation of the robot are compared to the data from the motion capture. Despite strong variations in the simulated results, the simulated robot kept a similar direction and was only a few centimetres o˙ from the real robot.",
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"title": "FPGARay: Accelerating Physically Based Rendering Using FPGAs",
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"abstract": "The synthesis of an image from a scene stored on a computer is called rendering, which is able to deliver photo-realistic results, e.g., by using specific variants of the class of ray tracing\nalgorithms. However, these variants (e.g., path tracing) possess a stochastic characteristic which results in a high computational expense. This is explained by the nature of stochastic algorithms, which use a high number of samples to compute a result—in case of ray tracing, these samples manifest in a high number of rays needed for a complete rendering.\n\nOne possibility to accelerate ray tracing—no matter if using a stochastic or simpler variants—is the use of customized hardware. FPGRay is such an approach, which combines the use of customized hardware with the software of an off-the-shelf PC to a hybrid solution. This allows increasing the efficiency by specialized hardware and delivers a sustainability in case of changing algorithms at the same time.\n\nThe results point towards a possible efficiency gain. Unfortunately, in the scope of this thesis this was not realizable and the specific implementation showed a lower efficiency compared to the software implementation. Nevertheless, the possibility to achieve a higher efficiency with this approach by indicating FPGRay’s potential could be shown.",
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"abstract": "The creation of visually pleasing artistic metro maps usually requires a designer and a lot of effort, and while the automatic generation of regular metro maps has been done via several methods, none focus on the artistic aspect. To make the process easier for designers this thesis introduces a method that automatically creates maps that can either be used as they are, or used as baseline for the future design process. The goal of this thesis is to find a method and based on that create a prototype that generates metro maps in arbitrary shapes that simply requires the map and contour as input. Additional parameters are supposed to allow a user to make adjustments if so desired. The general approach is to first prepare the map as well as the contour for the following least squares calculations that reshape the map in a way to fit the contour and then create the look of a typical metro map. To test the algorithm and showcase its results it is applied to two different maps and seven different shapes. These results indicate that the introduced approach is capable of creating metro maps in arbitrary shapes, but need further adjustments by a designer to finalize the map.",
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"abstract": "The Planetary Robotics 3D Viewer (PRo3D) is an interactive visualization tool thatallows for geological analyses of planetary surfaces. The primary goal is to supportgeologists at NASA and ESA in their mission to find signs of life on Mars by enablingthem to perform analyses on a high-resolution 3D surface model. While PRo3D facilitatesan exploratory workflow to gain new insights, there is a lack of support to communicatenew findings. In this thesis, we discuss the design and implementation of storytellingmechanisms into PRo3D that allow for an easy, fast, and interactive communicationof results. Moreover, we show how provenance information can be incorporated intostories, enabling geoscientists to present how they arrived at a certain discovery orinterpretation. Provenance includes the individual steps in the analysis process thatlead to a given finding, supporting its verification and reproducibility. We present abroad overview about storytelling and provenance in visualization, and discuss the designspace of a provenance-based storytelling approach in the context of geological analysesas conducted in PRo3D. Finally, we present a prototype as a proof of concept based onthese deliberations.",
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"abstract": "In this thesis, we introduce a web-based interactive exploration interface for a broadaudience to investigate large, weighted, bipartite graphs. The motivation of this workis based on theMedia Transparency Databasewhich arises from an Austrian law thatcompels legal entities to announce their advertisement spendings to media organizationsand meets the specified characteristics.Most current interactive exploration tools use complex visualizations because they weredeveloped for domain experts. As the Media Transparency Database is of potentialinterest to a broad audience, we provide a framework not just for domain experts butalso for inexperienced users.Therefore, we conducted systematic benchmarks to compare state-of-the-art web-basedrendering techniques. Furthermore, we compared the performance of different librariesto determine the most efficient rendering solution and current limitations of web-basedrendering.We introduce the concept of Firework Plots, which aims to provide a common visualizationthat scales well with the size of the data. Our visualization concept is based on intuitivenode-link visualization in combination with multiple visualization and interaction concepts.Hierarchical aggregation is used to improve scalability. Constrained, layered, force-basedgraph layouts, as well as firework animations and seamless zoom, are used to allowinexperienced users to drill down the graph hierarchy and track nodes through thehierarchy. Moreover, visibility management is used to reduce clutter and improveperformance.Based on the insights of our web-based graph rendering analysis, we implementedour framework and the concept of Firework Plots. We show the usefulness of theimplementation by discussing different use cases and comparing it to related work.Moreover, we conducted multiple benchmarks to show the rendering performance and calculation times.",
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"title": "BrainGait: Gait Event Detection and Visualization for Robotic Rehabilitation",
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"abstract": "Mobility impairment in adults is one of most prevalent types of disabilities in developed countries. Gait rehabilitation can be used to regain some or all motor functions, especially after a stroke. In recent years, robot-assisted gait training attracted increasing interest in rehabilitation facilities and scientific research. With this advent of robotic recovery comes the need to objectively measure the patient’s performance. Physiotherapists need essential information about the current status during training and how to improve the patient’s gait, presented in an easy to grasp and compact form. On the other hand, physicians rely on statistical measures in order to evaluate the patient’s progress throughout the therapy. This thesis discusses commonly used visualizations and statistics while proposing improvements and adaptations in the context of PerPedes, a novel robotic gait rehabilitation device. In order to measure the patient’s performance, a new algorithm for gait event detection was developed, based on force data from pressure plates. The following work demonstrates that standard algorithms fail with PerPedes, while the proposed solution can robustly handle highly distorted gait patterns, such as hemiplegic gait, foot drop, or walking backwards. The software application developed during this thesis provides feedback to the therapist and generates suggestions for gait improvement. Furthermore, gait statistics are inferred from each therapy session and collected in order to be used for future analysis and inter-patient comparison.",
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"abstract": "In many domains, the sheer quantity of text documents that have to be parsed increases\ndaily. To keep up with this continuous text stream, a considerable amount of time has to be invested. We developed a classification interface for text streams that learns user-specific topics from the user’s labeling process and partitions the incoming data into these topics.\nCurrent approaches that try to derive content categorization from a vast number of unstructured text documents use pre-trained learning models to perform text classification.\nThese models assign predefined categories to the text according to its content. Depending on the use case, a user’s interests might not coincide with the given categories. The model cannot adapt to changing terminology that was not present during training. Besides these factors, users often do not trust pre-trained models as they are a black box for them.\nTo solve this problem, our application lets users define a classification problem and\ntrain a learning model through interaction with a Star Coordinates visualization. The\napproach that makes this interaction efficient is a variant of active learning. This active learning variant states that a learning model can achieve greater accuracy with fewer labeled training instances, if a user provides data purposefully from which it learns. We adapted this strategy for text stream classification by visualizing the topic affiliation probabilities of the learning model and providing novel interaction tools to enhance the model’s performance iteratively. By simulating different selection strategies common in active learning, we found that our visual selection strategies correspond closely to the classic active learning selection\nstrategies. Further, users performed on par with the best simulated selection strategies in the results from our preliminary user study. Our evaluation concludes that there are benefits from incorporating information visualization into the active learning process.",
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"abstract": "The number of installed sensors to acquire data, for example electricity meters in smart grids, is increasing rapidly. The huge amount of collected data needs to be analyzed and monitored by transmission-system operators. This task is supported by visual analytics techniques, but traditional multi-dimensional data visualization techniques do not scale\nvery well for high-dimensional data. The main contribution of this thesis is a framework to efficiently examine and compare such high-dimensional data. The key idea is to divide the data by the semantics of the underlying dimensions into groups. Domain experts are familiar with the meta-information of the data and are able to structure these groups into a hierarchy. Various statistical properties are calculated from the subdivided data. These\nare then visualized by the proposed system using appropriate means. The hierarchy and\nthe visualizations of the calculated statistical values are displayed in a tabular layout.\nThe rows contain the subdivided data and the columns visualize their statistics. Flexible interaction possibilities with the visual representation help the experts to fulfill their analysis tasks. The tasks include searching for structures, sorting by statistical properties, identifying correlations of the subdivided data, and interactively subdivide or combine\nthe data. A usage scenario evaluates the design of the framework with a data set of the target domain in the energy sector.",
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"title": "Generating Synthetic Training Data for Video Surveillance Applications",
"date": "2019-10",
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"abstract": "Advanced rendering algorithms such as suggestive contours are able to depict objects in the style of line drawings with various levels of detail. How to select an appropriate level of detail is based on visual aesthetics rather than on substantial characteristics like the accuracy of 3D shape perception. The aim of this thesis is to develop a novel approach for effectively generating line drawings in the style of suggestive contours that are optimized for human 3D shape perception while retaining the amount of ink to a minimum. The proposed post-processing meta-heuristic for optimizing line drawings uses empirical thresholds based on probing human shape perception. The heuristic can also\nbe used to optimize line drawings in terms of other visual characteristics, e.g., cognitive load, and for other line drawings styles such as ridges and valleys.\nThe optimization routine is based on a conducted perceptual user study using the gauge figure task to collect more than 17, 000 high-quality user estimates of surface normals from suggestive contours renderings. By analyzing these data points, more in-depth understanding of how humans perceive 3D shape from line drawings is gained. Particularly the accuracy of 3D shape perception and shape ambiguity in regards to changing the level of detail and type of object presented is investigated. In addition, the collected data points are used to calculate two pixel-based perceptual characteristics: the optimal size of a local neighborhood area to estimate 3D shape from and the optimal local ink percentage in this area.\nIn the analysis, a neighborhood size of 36 pixels with an optimal ink percentage of\n17.3% could be identified. These thresholds are used to optimize suggestive contours\nrenderings in a post-processing stage using a greedy nearest neighbor optimization scheme.\nThe proposed meta-heuristic procedure yields visually convincing results where each\npixel value is close to the identified thresholds. In terms of practical application, the optimization scheme can be used in areas where high 3D shape understanding is essential such as furniture manuals or architectural renderings. Both the empirical results regarding shape understanding as well as the practical applications of the thesis’s results form the basis to optimize other line drawing methods and to understand better how humans\nperceive shape from lines.",
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"title": "Interactive Maps for Visualizing Optimal Route Planning Strategy",
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"abstract": "There are many situations in our everyday life like events, concerts, landmarks, attraction parks, etc. that often require from visitors to line-up in front of long queues and thus spend hours in waiting. An example of that are the Disneyland amusement parks. They are all very popular and attract a significant number of people every day. For this reason, the lining-up in front of attractions may cost much time – even up to a couple of hours. Despite that, the Disneyland parks are visited by millions of people every year [sta].\nSo to avoid so much waiting they need to make a plan in advance – when and in which\norder to visit the wanted attractions. However, to make such a plan, it could be very time consuming, difficult and even unpleasant, because many prerequisites need to be considered in advance. Having the main problems and annoyances described, the goal of this thesis is to create an assisting application. Its purpose is to give the visitors the possibility to create their own plan for their visit to Tokyo Disneyland. It contains two main assisting components. Firstly, an optimization algorithm calculating an optimized\nroute of the chosen attractions as well as a route visualization for an easy attraction finding. Both will reduce the time for lining-up and pre-planning. Such a technique will make it easier for visitors to see as many attractions as possible for a single day and thus, make the most of their visit.",
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"abstract": "A flood is an often unforeseen event, which can happen at any time and causes enormous\ndamage. Simulations are used to predict risks and respond to them early, like in the decision-support system Visdom. The real-time application is useful and offers many possibilities, e.g., simulations of the rising of the river over a certain time or the building of barriers. However, there is a lack of a true-to-life representation of the entire scene, which is important, as the visualization of the results must be understandable also for non-experts, such as decision-makers or the general public. More precisely, shadows are missing in the application so far, although they are particularly well suited to recognize\nrelations of objects to each other.\nThis bachelor thesis covers the implementation of shadows in Visdom to increase the realism of the scene. This is very complex for such big scenes on city or even country scale and requires many self-developed strategies, as there are no ready-made solutions.\nWe present an adaptation of the cascaded shadow maps algorithm, which provides a good way to subdivide the scene for better results in shadow quality. Further improvements are presented to increase the quality of the resulting shadows, as well as avoid occurring artifacts. The result of this work is a flexible visualization of soft shadows for a variety of different-sized scenes in real time, which increase the realism and spatial perception\nand further do not influence the performance too much.",
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"title": "Generating seating plan images using clustering and concave hull algorithms",
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"abstract": "This study presents a process of generating seating plan images for the Ticket Gretchen\napp. The app offers the ability to buy tickets for theaters and similar venues by using\nan interactive seating plan. A seating plan image is a venue’s abstract visualization\ndefined by the seating layout of a performance. It should give an impression of the spatial\nstructure to see which seats are in reach of each other. The proposed automated solution\nof generating these images replaces the previously used process of creating the seating\nplan images manually. The image is made up of polygons representing seat groups that\nshow the user which seats are near each other and which are separated from each other.\nThe grouping of seats is done with the DBSCAN clustering algorithm using the seats’ 2D\nposition, sector and box information. For the computation of the polygons two concave\nhull algorithms are compared.",
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"abstract": "Computer-aided visualisations are a powerful tool to make large datasets more accessible. Artificial intelligence (AI) also offers diverse ways in which to extract semantic values from large data stocks. It enables users to analyse records in ways that often exceed conventional methods in their specificity and accuracy.\nMedicine - more specifically those specialisations requiring imaging methods - are in need of sophisticated visualisation techniques. Our team at ImageBiopsy Lab [Lju17] runs development and research in the field of AI aided visualisations in medicine. For my thesis I developed a system for measuring the joint space in x-rays of the knee, based on existing concepts. Results of the measurements are processed and presented to the user as an augmented picture. This is achieved by employing different layers of graphical\noverlays on top of the original image. All measurements are based on parameters of the\nKellgren and Lawrence System (KLS) for classification of Osteoarthritis (OA).\nThe proposed method enables its users to asses the stage and tendency of OA in the\nknee at first glance as compared to conventional methods, which can be tedious and time-consuming. Calculated focus points in the mask layers can also be adjusted in\nreal time to accommodate for statistical outliers. The system was incorporated into\nan existing web-based framework which already demonstrates its potential in a clinical environment.",
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"title": "Web-Based Osteoarthritis-Analysis Generating Data from Native Libraries and Machine-Learning Models",
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"abstract": "As artificial intelligence (AI) progresses with seemingly unstoppable speed, its wide field of applications broadens by the day. One area where AI advancements appear to be\nespecially promising is their employment in the medical sector. Nowadays, due to the\nwider availability of processing power, algorithms based on neuronal networks can be used to generate far more data in areas where it previously seemed unthinkable.\nTraditional image-processing-algorithms often utilize computer vision (CV)-algorithms such as edge-detection to generate data from pixel input. While this method of gaining data worked well in the past, AI can help to improve the precision of such an analysis. The area I focussed on in this thesis is the generation of data from x-ray images of the knee joint. ImageBiopsy Lab (IB Lab)’s algorithms relied heavily on CV-based analysis\nfor the diagnosis of osteoarthritis (OA) in the knee. While this yielded good results in the past, this work will show that the use of deep neuronal networks improves accuracy in a significant way.\nFurther, neuronal networks can provide additional information that was a lot harder to be gained before, such as the laterality of a given image.\nThe aim of this project was to diagnose OA faster and more precisely than in the\npast and to embed it into a web-based solution for broader accessibility. To showcase the benefits of the described method, at the time of writing, our software is in the stage of\nbeing rolled out in a hospital in Lower Austria.\nBecause of the advancements mentioned above, this work will focus on the description and comparison of gaining information from x-ray images for a meaningful and efficient diagnosis of OA in the knee. ",
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"abstract": "This master thesis aims to provide an in-depth comparison of four texture algorithms\nin their capacity of discriminating patients with osteoarthritis (OA) from the ones without, recognizing early signs of Osteoarthritis and tracking disease progression from 2D radiographs of the knee trabecular bone (TB). Given the fractal properties of the trabecular bone (TB), two fractal-based algorithms (Bone Variance Value (BVV) and Bone Score Value (BSV)) that try to characterize the complexity of the underlying 3D structure of the bone are presented. The third algorithm (Bone Entropy Value (BEV), based on Shannon’s Entropy) stems from the information theory and aims to describe the bone structure in terms of information complexity. The last algorithm (Bone Coocurrence Value (BCV)) is based on the co-occurrence matrix of an image and describes the image texture in terms of certain Haralick features. If successful, such algorithms posses a great potential to lower the costs (financial, time) associated with the diagnosis of osteoarthritis (OA) through automation of the procedure, and with the treatment. The earlier treatments and risk reduction measures are less costly than the\nprocedures involved due to a more advanced stage of the disease (surgery, implants, etc.).\nFirst, a motivation for the detection of early osteoarthritis (OA) is given. Second, a detailed description and mathematical background of the algorithms are presented and validated on sample, artificial data. Third, the employed data sets used for classification tests are introduced. Fourth, the statistical methods and neural network models employed are presented and discussed. Fifth, the features produced by each algorithm are discussed and their independent and combined capacity of discriminating between bones with early signs of OA and healthy bones. Also the capacity of tracking OA progression\nthrough the years is quantified by statistical tests. Also in this part we present the best classification scores obtained from the most optimal neural networks for each use case. Finally, thoughts on future improvements and the generalization of the algorithms in other anatomical contexts, for other diseases or in other fields, like histology and\nmammography, are made.\nIn this work we show that the state-of-the-art in OA prediction can be surpassed by\nutilizing only models based on texture features alone. Our gender-stratified analysis produces a prediction score of 83% for males and 81% for females in terms of Area Under the Receiver Operating Characteristic Curve (ROC-AUC).",
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"abstract": "Breast cancer is the most common cancer with a high mortality rate. Neoadjuvant\nchemotherapie is conducted before surgery to reduce the breast tumor mass. Currently,\na lot of trials are taking place, with the purpose of understanding the effects of different chemotherapy strategies. In this work a software is developed to analyse and compare the influence of these treatments. The study data is available as 4D Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. To reduce the time of manual segmentation and the connection of segmented lesions over time a automatic procedure was implemented. This process uses the time-signal intensity curve and a support vector machine to classify\nlesions with calculated morphological features. To analyse the data, two views are available. The Intra-patient view visualizes the tumor behaviour of an individual patient over time. With the Multi-patient view the user is able to compare multiple patients’ lesions and additional added patient data. Both views are implemented with JavaScript and can be expanded easily. Because of missing ground truth an evaluation of the automatic segmentation method was not possible.",
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"abstract": "Analyzing large amounts of data is becoming an ever increasing problem. Bitcoin as an\nexample has produced more data than is possible to analyze. In order to compensate for these difficulties, creative ideas that employ data aggregation or minimization have been proposed. Other work also focuses on introducing novel visualization types that are geared towards the visualization of blockchain data. However, visualization of graphs through node-link diagrams remains a difficult challenge. Analysis of the Bitcoin transaction graph to follow bitcoin (BTC) transactions (TXs) poses a difficult problem due to the Bitcoin\nprotocol and the amount of data. This thesis combines two data processing strategies to visualize big network data on commodity hardware. The idea is to use visualization as a technique to analyze a data-set containing Bitcoin transaction information. Criminals use Bitcoin as a means of payment because of its guaranteed pseudonymity. Through visualization we aim to identify patterns that will allow us to deanonymize transactions. To do so we use a proxy server that does data preprocessing before they are visualized on a web client. The proxy leverages parallel computing to be able to do top-down and bottom-up data processing fast enough for interactive visualization. This is done through incremental loading (bottom-up), which enables to visualize data immediately\nwithout a (pre-)processing delay. The database containing the public Bitcoin ledger is over 163 gigabytes in size. The resulting graph has more than 800 million nodes. As this information is too much to be visualized, we also employ a top-down approach of data aggregation and graph minimization of the transactional graph. Through this methodology we intend to solve performance problems of long processing delays and the problem of fractured data where the data is shown only partially in the visualization.\nWe collaborate with security experts who share insights into their expertise through a continuously ongoing dialog. Exploratory analysis on a big data-set such as the Bitcoin ledger, enabled through the methodology presented in this thesis, will help security experts to analyze the money flow in a financial network that is used by criminals for its anonymity. We evaluate the result through the performance and feedback of these security experts as well as benchmark the performance against current best practice approaches.",
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"title": "Optimized Sorting for Out-of-Core Surface Reconstruction",
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"abstract": "In recent years the amount of acquisition methods for point clouds has been increasing consequently and it is getting more and more interesting for society. Even if it is possible to render point clouds directly, nowadays there exist many more algorithms which deal with triangle meshes than point clouds. For example 3D printer software requires watertight meshes as input. This makes automatic conversion of point sets to triangle meshes an important research topic. The aim of this Bachelor Thesis was to implement a plugin for Scanopy (a point cloud editing and rendering program) which can convert point clouds with hundreds of millions of samples in such a detailed degree that the data exceeds common main memory sizes. Therefore, an out-of-core algorithm was needed. The used out-of-core Poisson surface reconstruction approach requires the sorting of the input point samples in a preprocessing step. In this Bachelor Thesis it is shown that the sorting of the data with an optimized multithreaded merge sort algorithm can improve the total required time for the reconstruction process significantly. Further, this work indicates a problem which occurs while reconstructing meshes with a Poisson based reconstruction approach from scans of an open terrain. The problem leads to large unnecessary triangles which hide the reconstructed surface. A very basic solution approach for this problem is also stated.",
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"title": "BrainXPlore - Decision finding in Brain Biopsy Planning",
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"abstract": "Neurosurgeons make decisions based on expert knowledge that takes factors such as\nsafety margins, the avoidance of risk structures, trajectory length and trajectory angle into consideration. While some of those factors are mandatory, others can be optimized in order to obtain the best possible trajectory under the given circumstances. Through comparison with the actually chosen trajectories from real biopsies and qualitative interviews with domain experts, we identified important rules for trajectory planning. In this thesis, we present BrainXplore, an interactive visual analysis tool for aiding\nneurosurgeons in planning brain biopsies. BrainXplore is an extendable Biopsy\nPlanning framework that incorporates those rules while at the same time leaving full\nflexibility for their customization and adding of new structures at risk. Automatically computed candidate trajectories can be incrementally refined in an interactive manner until an optimal trajectory is found. We employ a spatial index server as part of our system that allows us to access distance information on an unlimited number of risk structures at arbitrary resolution. Furthermore, we implemented InfoVis techniques such as Parallel Coordinates and risk signature charts to drive the decision process. As a case study, BrainXPlore offers a variety of information visualization modalities to present multivariate data in different ways. We evaluated BrainXPlore on a real dataset and accomplished acceptable results. The participating neurosurgeon gave us the feedback that BrainXPlore can decrease the time needed for biopsy planning and aid novice users in their decision making process.",
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"title": "Evaluation of Machine Learning Frameworks on Tuberculosis Classification of Chest Radiographs",
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"abstract": "In this thesis different state-of-the-art machine learning frameworks were implemented and evaluated on chest radiographs to classify them into tuberculotic or healthy radiographs.\nTraditional explicit feature engineering was performed, as well as different deep learning approaches were applied. For the deep learning experiments different publicly available architectures were compared in two different tasks. The first task with deep learning was to use a Convolutional Neural Network, already trained on a different task, to extract\nfeatures of the chest radiographs. These features were then classified separately. The second experiment was to use a Convolutional Neural Network, again pretrained on a different task, and train this network carefully again on the chest radiographs. The results of the different frameworks were summarized, evaluated and presented in tables.",
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"abstract": "Computer-generated video sequences with a frame-rate higher than the usual 24 images per second, such as 48 or 60 frames per second, have become more popular in the respective industries, due to more visual fidelity. This, however, results in more computational costs for the same length of the video sequence.\n\nOne solution to this problem is the so-called frame-rate upsampling, which makes use of temporal and spatial coherence to approximate new frames and therefore saves computational time. Several methods have been published in this field, for the purposes of real-time rendering as well as for offline rendering algorithms. \n\nIn this thesis, two new algorithms for fame-rate upsampling are introduced. Those are targeted at high-quality computer-generated images that feature various globalillumination effects. The two new algorithms make use of a video denoising method - the non-local means algorithm - to find the appropriate pixel colors for the frame, that has to be upsampled. To find the corresponding pixels in another frame, the methods of this thesis either use existing color information or require additional data, which can be extracted from any global-illumination algorithm with minimal further computations. The proposed methods are aimed at handling reflections and refractions in the scene\nbetter than previous work.",
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"title": "Quantifying the Convergence of Light-Transport Algorithms",
"date": "2017-11-14",
"abstract": "This work aims at improving methods for measuring the error of unbiased, physically\nbased light-transport algorithms. State-of-the-art papers show algorithmic improvements\nvia error measures like Mean Square Error (MSE) or visual comparison of equal-time\nrenderings. These methods are unreliable since outliers can cause MSE variance and\nvisual comparison is inherently subjective.\nWe introduce a simple proxy algorithm: pure algorithms produce one image corresponding\nto the computation budget N. The proxy, on the other hand, averages N independent\nimages with a computation budget of 1. The proxy algorithm fulfils the preconditions\nfor the Central Limit Theorem (CLT), and hence, we know that its convergence rate is\n(1/N). Since this same convergence rate applies for all methods executed using the\nproxy algorithm, comparisons using variance- or standard-deviation-per-pixel images are\npossible. These per-pixel error images can be routinely computed and allow comparing\nthe render quality of different lighting effects. Additionally, the average of pixel variances\nis more robust against outliers compared to the traditional MSE or comparable metrics\ncomputed for the pure algorithm.\nWe further propose the Error Spectrum Ensemble (ESE) as a new tool for evaluating lighttransport\nalgorithms. It summarizes expected error and outliers over spatial frequencies.\nESE is generated using the data from the proxy algorithm: N error images are computed\nusing a reference, transformed into Fourier power spectra and compressed using radial\naverages. The descriptor is a summary of those radial averages.\nIn the results, we show that standard-deviation images, short equal-time renderings, ESE\nand expected MSE are valuable tools for assessing light-transport algorithms.",
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"abstract": "We have developed two different test setups allowing the characterization of noise in X,\nY and Z direction for the KinectV2 and the Phab2Pro depth sensors. We have combined\nthese two methods, generating a single noise model allowing a prediction of the amount of\nnoise in specific areas of an image in the three respective directions at a certain distance\nand rotation. We have conducted two test setups and measured the noise from 900 mm\nto 3.100 mm for the generation of the noise models. The test setup of this thesis focused\non determining the noise in X, Y and Z direction, covering the whole frustum of the\nrespective depth sensor. In this thesis, Z noise was measured against a wall and X and\nY noises were measured using a 3D chequerboard that was shifted through the room,\nallowing the above mentioned coverage of the whole frustum. Along the edges of the\ncells of the chequerboard, the X and Y noise was measured. The combined model was\nevaluated by using a solid cube to classify the quality of our noise model.",
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"abstract": "Image segmentation is an important processing step in various applications and crucial in the medical field. When a new segmentation technique is introduced, validation and evaluation are essential for medical image analysis. But the automation of these processes is still not sufficient. Many algorithms have been published but there is still no satisfying way to assess whether an algorithm produces more accurate segmentations than another. More effort is spent on the development of algorithms than on their evaluation and therefore many researchers use the less complex subjective methods. For these techniques multiple experts are needed to visually compare several segmentation results, which is a very time-consuming process. Another way of comparing different results is the supervised evaluation method. Here we need experts, who manually segment reference images, which are used for comparison. As seen in recent researches there is a need for unsupervised methods due to many applications, in which user assistance is\ninfeasible. The aim of this thesis is to provide an environment to visually and objectively evaluate segmentation results in the field of vessel segmentations. Our framework enables the comparison at voxel-level with various visualization techniques and objective measurements. These methods are meant to make the comparison more understandable for users. A subjective evaluation is realized through a comparative visualization by using a two- and three-dimensional comparison of voxels. Another general overview is provided by a maximum-intensity projection, which highlights the vessel structure. As purely\nobjective evaluation technique, various metrics are used, to assure independence from experts or a ground truth. By using these techniques this paper presents an approach for evaluating differences in medical images, which does not rely on a permanent presence of an expert.",
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"abstract": "This thesis describes a technique for editing segmentation results of vessels, which should enhance usage and reduce work duration for physicians by using a simple and fast way of interaction. Moreover also a quick calculation of an accurate result was of primary interest. Since vascular structures are vulnerable to diseases, vessels are the main focus of this thesis. Nowadays, Image Analysis is able to facilitate the medical diagnosis procedure.\nSince stroke treatment is time-crucial, appropriate algorithms should be fast and enable an accurate depiction of the arteries to simplify the diagnostic process. However, because automatic segmentation is often quite inaccurate and manual segmentation is tedious, neither of these two methods alone is often adequate for usage. Because of this we suggest to combine the fast automatic segmentation and the exact manual editing done by clinical experts. To reduce effort and working time of the medical staff, this thesis describes different techniques, which were developed to modify and, more importantly, to improve\nthe segmentation results. The segmentation mask can be altered as its components can be separately removed and independent elements can be connected. A framework was implemented, with which a user is able to perform these tasks interactively. The deletion process is supported by various metrics, which enable the search and removal of similar structures. Also this framework assists the reconnection of vessels by finding the most likely connection by the means of image intensities and their gradients. The main goal of this thesis was to facilitate and accelerate the editing process by implementing fast semi-automatic algorithms. Intuitive interaction methods also had a major impact on the design.",
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"abstract": "Thumbnails are used to display lists of open windows or tabs when switching between\nthem on computers and on mobile devices. These images make it easier to recognize the\nopened applications, and help to find the needed window quicker. Thumbnails however\nonly display a screenshot of the windows, so they get potentially confusing if there are\nmore opened windows or if the same application is opened multiple times. Depending\non the resolution of the display, the screenshot size decreases as the number of opened\nwindows increases. Furthermore, within the same application (like MS Office World)\nthe screenshots are similar in appearance (e.g. : white paper and tool bar), but the\nimportant text is not readable. There are several approaches that filter the important\nareas of the images to enhance the main region. In this bachelor thesis an application is\nimplemented that uses the above methods on screenshots. Screenshots of windows are\nreduced by cropping the irrelevant elements of the margin area using seam carving, i.e.\nby eliminating the non-important pixel paths; and by common down-sampling. As a\nresult the thumbnails show only relevant information, which makes them more expressive\nand easier to fulfill their purpose.",
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"title": "Automatic Breast Lesion Examination of DCE-MRI Data Based on Fourier Analysis",
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"abstract": "Breast cancer is the second most common cancer death among women in developed\r\ncountries. In less developed countries it has a mortality rate of about 25% rendering it the most common cancer death. It has been demonstrated that an early breast cancer diagnosis significantly reduces the mortality. In addition to mammography and breast\r\nultrasound, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is\r\nthe modality with the highest sensitivity for breast cancer detection. However, systems for automatic lesion analysis are scarce. This thesis proposes a method for lesion evaluation without the necessity of tumor segmentation. The observer has to define a Region Of Interest (ROI) covering the lesion in question and the proposed system performs an automated lesion inspection by computing its Fourier transform. Using the Fourier\r\ntransformed volume we compute the inertia tensor of its magnitude. Based on the gathered information, the Göttinger score, which is a common breast cancer analysis scheme, is computed and the features are presented in newly create plots. These plots\r\nare evaluated with a survey where radiologists participated. The Göttinger score assigns a numeric value for the following features: shape, boundary, Internal Enhancement\r\nCharacteristics (IEC), Initial Signal Increase (ISI) and Post Initial Signal (PIS). We tested our method on 22 breast tumors (14 malignant and 8 benign ones). Subsequently, we compared our results to the classification of an experienced radiologist. The automatic\r\nboundary classification has an accuracy of 0.818, the shape 0.773 and the IEC 0.886 compared to the radiologist’s results. An evaluation of the accuracy of the benign vs. malignant classification shows that the method has an accuracy of 0.682 for all\r\nthe Göttinger score features and 0.772 using only the shape, boundary and IEC. The evaluation of the plot shows that radiologist like the visual representation of the Göttinger\r\nscore for single lesions, they, however, refuse the plots where multiple lesions are presented\r\nin one visual representation.",
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"abstract": "This bachelor’s thesis is about a depth of field rendering algorithm using point splatting on per-pixel layers. The intuitive procedure just uses specific adjustable parameters for the lens of a camera to generate the desired effect in real-time. Using nothing but the depth information of a scene, the depth of field can be computed.\r\nThe so called circles of confusion are the representing value of the blur rate for objects that are out of focus. Depending on the distance between the focal plane and the current pixel, an expansion takes place because of the redirecting nature of a lens. The following required information is retrieved using an intensity distribution function.\r\nTo avoid intensity leakage and provide a full opaque picture, the entire effect is done discrete on the screen space. This prevents subsampling and enables almost correct alpha value normalization.\r\nAs stated before, an intensity distribution function is required, but in a discrete form. This is an addition to the paper, where that function is preprocessed in the application and provided for the shader directly. A high resolution texture of the desired intensity distribution function is provided as input to a shader program that renders multiple discrete versions. Another further addition prepares the texture by normalization, consuming more memory, but saving lots of time during computation.\r\nThe last part of this paper includes a defocused highlights implementation, where a tone-mapping is applied to mimic them. In general, the luminance of a pixel is computed by a simple formula, summing up the three colors with adjustment factors. Depending on this value and some parameters, the color is adjusted where bright regions are enhanced to construct this effect.\r\nThis thesis implements and extends this work and is used to test its performance after seven years when it was published. Using different configurations for the effect, the provided stable framerates of the results are used as metric for benchmarking.\r\n",
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"abstract": "Programming the GPU is more important than ever, but the organization and development of shader code for the GPU is a difficult task. Can this process be embedded into the high level language C#, gain from the features of its toolchain and enrich shader development?\n\nFor this purpose this thesis describes the design and implementation of a framework to abstract and embed shader development into C# with an internal domain-specific language (iDSL\nfor short) as front-end and a plug-in system in the back-end to support expandable optimizations and different shader languages as targets.\n\nThe implemented framework fits shader development into the C# toolchain, supporting autocompletion, and type error checking in the editor. The system offers good modularity and\nencourages developing shaders in reusable parts.\n\nThis diploma thesis was developed in cooperation with VRVis Research Center in Vienna, Austria.",
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"abstract": "Parameters and the process of setting them play a major role in the world of computer based visualisation, no matter whether it is a visualisation of information or of volume data. Finding suitable parameter values can take up most of the time in the visualisation process and users have to sensibly adjust a large number of parameters. Finding a useful parameter value\r\ndistribution for achieving the desired visualisation result can be a cumbersome process which also depends on the user’s speed and experience. The purpose of this master’s thesis is to find a new and faster way to reach an appropriate parameter value distribution resulting in the desired\r\nvisualisation.\r\n\r\nFor this master’s thesis a prototype is developed which guides the user through a semi-automatic process of adjusting parameter values, which finally results in the desired visualisation of a scientific volume. Using this prototype enables the users to explore a large number of different parameter values within only a few iterations steps and a short amount of time. In order to do\r\nso we move away from the classic approach of setting parameters by adjusting sliders or combo boxes.\r\n\r\nThe idea of this thesis is to combine concepts that were already used in volume visualisation into a prototype. Our main strategy is to present pre-rendered images of the volume with different parameter values to the users. The images that are closest to the target visualisation can be selected and new images, similar to these, are shown. After some iterations of this process the users should have reached a visualisation that meets their expectations. The basis of our\r\napproach is a spreadsheet user interface.\r\n\r\nFurther we make use of the concept of high-level parameters, which are a combination of lowlevel parameters, like the specular exponent, to one single parameter, like contrast. The advantage of this concept is to have parameters which are more understandable to the users. We move\r\naway from the concept of displaying every single image in the spreadsheet interface, having multiple pages. Instead we use kMeans++ or DBScan with an automatic method to choose the\r\ndistance parameter ? to cluster the images by similarity. This results in only the cluster centres, which are images, being presented to the user in the spreadsheet interface for exploration. Additionally, Locally Linear Embedding (LLE) is used to map single images into a global coordinate\r\nsystem. As a second new approach we use the distance between the images within the coordinate system as a similarity measure for kMeans++ and DBScan. To provide a fast calculation of the\r\nLocally Linear Embedding, which includes the nearest neighbours, the distance matrix and the Eigenvalues of the images, we use CUDA. The selection process consists of two different steps:\r\nexploration and refinement. Depending on the cluster size of the selected image, a re-clustering of the sub cluster is done if the user has reached the end of the cluster due to having explored all images and not achieving the desired final image. Thus a new set with varied parameter values is\r\ncreated and used to render new images. In contrast to the initially created set, the newly created one takes into account the explored parameter values from the images chosen by the user. This\r\nmeans that the range - in which the values of the single parameters are varied - is limited by the minimum and maximum value the parameter received during the before made exploration. Our\r\ntests showed that that by combining all these techniques it is possible to explore many different parameter values for high-level parameters in a very short time, and to achieve visualisations equal to those created by setting parameter values manually. In a short test our approach enabled\r\ntwo users, who are rather inexperienced in the field of volume visualisation, to create similar visualisations in fewer steps than by setting parameter values manually.",
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"title": "AngioVis Patient Persistency Object Relation Mapping for Large Relational Datasets of Binary Data",
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"abstract": "A usual workstation in medicine processes a significant amount of patients per day. It is critical\r\nin a clinical daily routine, to retrieve, store and access analysed data in a fast pace. An application\r\nin this context needs to be responsive and speed up current processes. Due to the necessity of storing all these data, current development in database technologies provides opportunities\r\nto improve their management. In this thesis, we will investigate, how such a technologies can be transferred to a specific application scenario. Furthermore there are several application parameters, like load time, response delay and integrity of the stored data that can be improved,\r\nto have a positive impact on a medical workflow. Apart those performance related parameters there are several other factors like extendibility, scalability and structure that are taken into consideration\r\nas well.",
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"abstract": "The automatic generation of panoramic image mosaics has evolved into a mainstream consumer\r\napplication in recent years, thanks to modern algorithms and methods that can generate beautiful-\r\nlooking, artifact-free panoramas with minimal user interaction. Simple panoramas are only one\r\npossible application, other examples such as the scene-aware Google Street View show what is\r\npossible using these technologies. Software libraries such as\r\nOpenCV allow integration of image\r\nstitching and related operations into a multitude of applications with relative ease.\r\nIn this thesis, we present an interactive application (Pano.NET) that allows the user to\r\nexperiment with several commonly used methods for camera calibration and image stitching (as\r\nimplemented in\r\nOpenCV), and export related data such as registration information. We also give\r\nan overview of the used algorithms.\r\nIn addition, we implemented a projective drawing system that allows the user to draw lines\r\nand polygons onto a panorama and on the views that it consists of. Finally, a method is presented\r\nthat can generate six-sided cube maps out of a spherical panorama.",
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"title": "Procedural Textures for Architectural Models ",
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"abstract": "Texturing plays an extremely important role in computer graphics. They may for example give flat objects colour patterns and depth or roughness. However it is often difficult to find or create fitting textures for a geometry or surface. Creating textures by hand can prove extremely difficult as problems with seams or distortion may occur, which make the texture look unrealistic. In order to counter these problems many different kinds of texture synthesis have been developed among which procedural textures hold an important position.\n\nThe types of texture synthesis methods vary from example-based synthesis to procedural noise, cellular textures and more, which can produce a wide array of different results.\n\nThe aim of this paper is to introduce the reader into related topics of texture synthesis and to introduce a method for generating cellular brick pattern textures that represent brick bonds that are used in real architecture taking correct handling of wall and window borders into consideration. The method uses few parameters to control the synthesis process. In addition a method for generating random stone patterns is introduced. ",
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"title": "Visual Feature Exploration for ssTEM Image Segmentation",
"date": "2013-08",
"abstract": " In order to (preferably) automatically derive the neuronal structures from brain tissue image stacks, the research field computational neuroanatomy relies on computer assisted techniques such as visualization, machine learning and analysis. The image acquisition is based on the so-called transmission electron microscopy (TEM) that allows resolution that is high enough to identify relevant structures in brain tissue images (less than 5 nm per pixel). In order to get to an image stack (or volume) the tissue samples are sliced (or sectioned) with a diamond knife in slices of 40 nm thickness. This approach is called serial-section transmission electron microscopy (ssTEM). The manual segmentation of these high-resolution, low-contrast and artifact afflicted images would be impracticable alone due to the high resolution of 200,000 images of size 2m x 2m pixel in a cubic centimeter tissue sample. But, the automatic segmentation is error-prone due to the small pixel value range (8 bit per pixel) and diverse artifacts resulting from mechanical sectioning of tissue samples. Additionally, the biological samples in general contain densely packed structures which leads to non-uniform background that introduces artifacts as well. Therefore, it is important to quantify, visualize and reproduce the automatic segmentation results interactively with as few user interaction as possible.\r\n\r\nThis thesis is based on the membrane segmentation proposed by Kaynig-Fittkau [2011] which for ssTEM brain tissue images outputs two results: (a) a certainty value per pixel (with regard to the analytical model of the user selection of cell membrane pixels) which states how certain the underlying statistical model is that the pixel is belonging to the membrane , and (b) after an optimization step the resulting edges which represent the membrane. In this work we present a visualization-assisted method to explore the parameters of the segmentation. The aim is to interactively mark those regions where the segmentation fails to the expert user in order to structure the post- or re-segmentation or to prove-read the segmentation results. This is achieved by weighting the membrane pixels by the uncertainty values resulting from the segmentation process.\r\n\r\nWe would like to start here and employ user knowledge once more to decide which data and in what form should be introduced to the random forest classifier in order to improve the segmentation results either through segmentation quality or segmentation speed. In this regard we use focus our attention especially on the visualizations of the uncertainty, the error and multi-modal data. The interaction techniques are explicitly used in those cases where we expect the highest gain at the end of the exploration. We show the effectiveness of the proposed methods using the freely available ssTEM brain tissue dataset of the drosophila fly. Because we lack the expert knowledge in the field of neuroanatomy re must rely our assumptions and methods on the underlying ground truth segmentations of the drosophila fly brain tissue dataset. ",
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"title": "DICOM Web Viewer",
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"abstract": "The aim of this thesis is to introduce a web-based and open source DICOM viewer. Web technologies have already been successfully used in medical informatics. Since software used for medical applications is commonly proprietary and mostly restricted to certain operation systems, the combination of web technologies together with the DICOM standard is a natural next step. For this reason, this Bachelor's Thesis attempts to lay the basis for such an open source project. Digital Imaging and Communication in Medicine (DICOM) is an established and nonproprietary standard for the storage and the exchange of information in medical imaging. DICOM defines not only a data format, but it specifies also a network protocol based on the ISO/OSI-model. This work provides first an overview of the DICOM's history and describes its main concepts. Health Level 7 (HL7) is an industry standard for the data exchange between different hospital information systems. Integrating the Healthcare Enterprise (IHE) is an initiative that aims to harmonize the exchange of data between information systems in health by promoting common standards such as DICOM and HL7. Based on a clinical workflow, the interaction of DICOM and HL7 by using IHE is shown. The DICOM Web Viewer was designed as a \"Rich Internet Application\" and developed in JavaScript, HTML5 and CSS3. To create an easy to use user interface, state-of-the-art DICOM viewers were investigated and a common set of features were identified. DICOM files are parsed directly in the browser using JavaScript and then visualized using the HTML5