Liste der Fachpublikationen

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Gao, Shan; Ye, Qixiang; Liu, Li; Kuijper, Arjan; Ji, Xiangyang

A Graphical Social Topology Model for RGB-D Multi-Person Tracking

2021

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

Tracking multiple persons is a challenging task especially when persons move in groups and occlude one another. Existing research have investigated the problems of group division and segmentation; however, lacking overall person-group topology modeling limits the ability to handle complex person and group dynamics. We propose a Graphical Social Topology (GST) model in the RGB-D data domain, and estimate object group dynamics by jointly modeling the group structure and states of persons using RGB-D topological representation. With our topology representation, moving persons are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctively associated and the cohesion of each group to be precisely modeled. Using the learned typical topology pattern and group online update modules, we infer the birth/death and merging/splitting of dynamic groups. With the GST model, the proposed multi-person tracker can naturally facilitate the occlusion problem by treating the occluded object and other in-group members as a whole, while leveraging overall state transition. Experiments on different RGB-D and RGB datasets confirm that the proposed multi-person tracker improves the state-of-the-arts.

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A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet

2021

IEEE Transactions on Visualization and Computer Graphics

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.

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Hashisho, Yousif; Dolereit, Tim; Segelken-Voigt, Alexandra; Bochert, Ralf; Vahl, Matthias

AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production

2021

Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <16, 2021, online>

Shrimp farming is a century-old practice in aquaculture production. In the past years, some improvements of the traditional farming methods have been made, however, it still involves mostly intensive manual work, which makes traditional farming a neither time nor cost efficient production process. Therefore, a continuous monitoring approach is required for increasing the efficiency of shrimp farming. This paper proposes a pipeline for automated shrimp monitoring using deep learning and image processing methods. The automated monitoring includes length estimation, assessment of the shrimp’s digestive tract and counting. Furthermore, a mobile system is designed for monitoring shrimp in various breeding tanks. This study shows promising results and unfolds the potential of artificial intelligence in automating shrimp monitoring.

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Analyzing and Improving the Parametrization Quality of Catmull-Clark Solids for Isogeometric Analysis

2021

IEEE Computer Graphics and Applications

In the field of physically based simulation, high quality of the simulation model is crucial for the correctness of the simulation results and the performance of the simulation algorithm. When working with spline or subdivision models in the context of isogeometric analysis, the quality of the parametrization has to be considered in addition to the geometric quality of the control mesh. Following Cohen et al.'s concept of model quality in addition to mesh quality, we present a parametrization quality metric tailored for Catmull-Clark (CC) solids. It measures the quality of the limit volume based on a quality measure for conformal mappings, revealing local distortions and singularities. We present topological operations that resolve these singularities by splitting certain types of boundary cells that typically occur in interactively designed CC-solid models. The improved models provide higher parametrization quality that positively affects the simulation results without additional computational costs for the solver.

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Automatic Model-based 3-D Reconstruction of the Teeth from five Photographs with Predefined Viewing Directions

2021

Medical Imaging 2021: Image Processing

SPIE Medical Imaging Symposium <2021, online>

Proceedings of SPIE
11596, 1

Misalignment of teeth or jaws can impact the ability to chew or speak, increase the risk of gum disease or tooth decay, and potentially inuence a person's (psychological) well-being. Orthodontic treatments of misaligned teeth are complex procedures that employ dental braces to apply forces in order to move the teeth or jaws to their correct position. Photographs are typically used to document the treatment. An automatic analysis of those photographs could support the decision making and monitoring process. In this paper, we propose an automatic model-based end-to-end 3-D reconstruction approach of the teeth from _ve photographs with prede_ned viewing directions (i.e. the photographs used in orthodontic treatment documentation). It uses photo- or view-speci_c 2- D coupled shape models to extract the teeth contours from the images. The shape reconstruction is then carried out by a deformation-based reconstruction approach that utilizes 3-D coupled shape models and minimizes a silhouette-based loss. The optimal model parameters are determined by an optimization which maximizes the overlaps between the projected 2-D outlines of individual teeth of the 3-D model and the contours extracted from the corresponding photograph. After that the point displacements between the projected outline and segmented contour are used to iteratively deform the 3-D shape model of each tooth for all _ve views. Back-projection into shape space ensures that the 3-D coupled shape model consists of (statistically) valid teeth. Evaluation on 22 data sets shows promising results with an average symmetric surface distance of 0.848mm and an average DICE coe_cient of 0.659.

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Oyarzun Laura, Cristina; Hartwig, Katrin; Distergoft, Alexander; Hoffmann, Tim; Scheckenbach, Kathrin; Brüsseler, Melanie; Wesarg, Stefan

Automatic Segmentation of the Structures in the Nasal Cavity and the Ethmoidal Sinus for the Quantification of Nasal Septal Deviations

2021

Medical Imaging 2021: Computer-Aided Diagnosis

SPIE Medical Imaging Symposium <2021, online>

Proceedings of SPIE
11597

Nasal septal deviations are a well-known and widespread problem. According to the American Academy of Otolaryngology 80% of the population have a nasal septal deviation. Its level of severity can range from the person not being aware of it to respiratory obstruction and choking. It is therefore necessary to distinguish those patients at risk. For a proper diagnosis, the amount and location of the deviation have to be considered, but also the shape and changes in the surrounding turbinates. The segmentation of the structures of interest is an important step to reduce subjectivity in the diagnosis. Unfortunately, due to their variable and tortuous shape manual segmentation is time consuming. In this paper, the _rst method for the automatic segmentation of the structures in the nasal cavity and ethmoidal sinus is presented. A coupled shape model of the nasal cavity and paranasal sinus regions is trained and used to detect the corresponding regions in new CT images. The nasal septum is then segmented using a novel slice-based propagation technique. This segmentation allows the additional separation and segmentation of the left and right nasal cavities and ethmoidal sinuses and their structures by means of an adaptive thresholding with varying boundary sizes. The method has been evaluated in 10 CT images obtaining promising results for the nasal septum (DICE: 87.71%) and for the remaining structures (DICE: 72.01% - 73.01%). Based on the resulting segmentations, a web-based diagnosis tool has been designed to quantify the septal deviation using three metrics proposed by clinical experts.

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Endl, Barbara Sophie; Kuijper, Arjan [Prüfer]; Boine-Frankenheim, Oliver [Prüfer]; Schmitz, Benedikt [Betreuer]

Automatisierte Bilderverarbeitung für Ionenstrahldiagnostik

2021

Darmstadt, TU, Bachelor Thesis, 2021

Ziel der vorliegenden Arbeit ist es, die momentan manuelle bildtechnische Auswertung radiochromatischer Filme (kurz RCFs), die bei der Charakterisierung von laserbeschleunigten Ionenstrahlen zum Einsatz kommen, zu automatisieren.

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Boche, Benjamin; Kuijper, Arjan [1. Gutachten]; Gorschlüter, Felix [2. Gutachten]

Comparing RGBD-based 6D Pose Estimation

2021

Darmstadt, TU, Bachelor Thesis, 2021

In this work the suitability of the pose estimation method "PVN3D", by He et al.[3], is evaluated for industrial applications. It first gives an overview over the research eld of 6D pose estimation. Starting by explaining the basics of the eld this work goes over point pair features, machine learning and evaluation metrics as a background knowledge for the 6D pose estimation methods of He et al.[3] and Vidal et al.[2, 23] and the 6D pose estimation benchmark "BOP" introduced by Hodan et al. [6, 16]. The main contribution of this thesis is the evaluation of PVN3D[3] on the BOP[6] framework, so it can be compared to a multitude of other pose estimators, e.g. the methods benchmarked in the BOP 2020 challenge[16]. Here we focus on the T-LESS[11] dataset, as provided by BOP[16], it provides images of objects typical of industrial applications. We found that PVN3D[3] performs worst on T-LESS[11] compared to the BOP-results from 2020[16]. Specially it performs worse than the PPF based method by Vidal et al.[2, 23], itself ranking 6th in 2020[16]. However, it has to be noted, that our experiments were not fully conclusive, as there are strong indications that there is an unknown bug in the training of PVN3D[3].

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Cross-database and Cross-attack Iris Presentation Attack Detection Using Micro Stripes Analyses

2021

Image and Vision Computing

With the widespread use of mobile devices, iris recognition systems encounter more challenges, such as the vulnerability of Presentation Attack Detection (PAD). Recent works pointed out the contact lens attacks, especially images captured under the uncontrolled environment, as a hard task for iris PAD. In this paper, we propose a novel framework for detecting iris presentation attacks that especially for detecting contact lenses based on the normalized multiple micro stripes. The classification decision is made by the majority vote of those micro-stripes. An in-depth experimental evaluation of this framework reveals a superior performance in three databases compared with state-of-the-art (SoTA) algorithms and baselines. Moreover, our solution minimizes the confusion between textured (attack) and transparent (bona fide) presentations in comparison to SoTA methods. We support the rationalization of our proposed method by studying the significance of different pupil-centered eye areas in iris PAD decisions under different experimental settings. In addition, extensive cross-database and cross-attack (unknown attack) detection evaluation experiments are demonstrated to explore the generalizability of our proposed method, texture-based method, and neural network based methods in three different databases. The results indicate that our Micro Stripes Analyses (MSA) method has, in most experiments, better generalizability compared to other baselines.

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Deep Learning Models for Optically Characterizing 3D Printers

2021

Optics Express

Multi-material 3D printers are able to create material arrangements possessing various optical properties. To reproduce these properties, an optical printer model that accurately predicts optical properties from the printer’s control values (tonals) is crucial. We present two deep learning-based models and training strategies for optically characterizing 3D printers that achieve both high accuracy with a moderate number of required training samples. The first one is a Pure Deep Learning (PDL) model that is essentially a black-box without any physical ground and the second one is a Deep-Learning-Linearized Cellular Neugebauer (DLLCN) model that uses deep-learning to multidimensionally linearize the tonal-value-space of a cellular Neugebauer model. We test the models on two six-material polyjetting 3D printers to predict both reflectances and translucency. Results show that both models can achieve accuracies sufficient for most applications with much fewer training prints compared to a regular cellular Neugebauer model.

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Uecker, Marc; Linnhoff, Clemens [Supervisor]; Kuijper, Arjan [Supervisor]

Development of a Method for the Fusion of Environment Perception Sensor Data for an Automated Vehicle

2021

Darmstadt, TU, Master Thesis, 2021

Autonomous driving is currently one of the most anticipated future technologies of the automotive world, and researchers from all over the world are dedicated to this task. In the same pursuit, the aDDa project at TU Darmstadt is a collaboration of researchers and students, focused on jointly engineering a car into a fully autonomous vehicle. As such, the aDDa research vehicle is outfitted with a wide array of sensors for environment perception. Within the scope of the aDDa project, this thesis covers the fusion of data from LIDAR, RADAR and camera sensors into a unified environment model. Specifically, this work focuses on providing real-time environment perception, including fusion and interpretation of data from different sensors using only on-board hardware resources. The developed method is a software pipeline, consisting of an analytic low-level sensor fusion stage, a 3D semantic segmentation model based on deep learning, and analytical clustering and tracking methods, as well as a proof-of-concept for estimating drivable space. This method was designed to maximize robustness, by minimizing the influence of the used machine learning approach on the reliability of obstacle detection. The sensor fusion pipeline runs in real-time with an output frequency of 10 Hz, and a pipeline delay of 120 to 190 milliseconds in the encountered situations on public roads. An evaluation of several scenarios shows that the developed system can reliably detect a target vehicle in a variety of real-world situations. The full contributions of this work not only include the development of a sensor fusion pipeline, but also methods for sensor calibration, as well as a novel method for generating training data for the used machine learning approach. In contrast to existing manual methods of data annotation, this work presents a scalable solution for annotating real-world sensor recordings to generate training data for 3D machine perception approaches for autonomous driving.

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Jestädt, Sebastian; Kuijper, Arjan [1. Gutachten]; Mukhopadhyay, Anirban [2. Gutachten]

Development of an AI Web Application to Train Radiologists in Deep Learning

2021

Darmstadt, TU, Master Thesis, 2021

In current news, the topic of AI is more present than ever. Accordingly, new studies on the subject are constantly being published. Similarly, the topic is also gaining more and more significance in digital healthcare. However, not everybody is prepared to work with the new technology. Based on this, a web application should be developed in this work to make radiologists “AI-ready”. For this reason, the state of the art is first discussed by presenting studies that deal with AI and radiologists. These illustrate the current state of knowledge and how radiologists see the future with AI. Especially, is shown which methods already exist to prepare them for this new technology. In the second part, currently existing web applications are presented and critically evaluated. Also included a work that developed a neural network for predicting pneumothorax disease. Next, a survey is conducted within the thesis to provide an overview of how easy it is for entry-level enthusiastic radiologists to learn about AI. Based on the findings, an analysis is carried out, which underlines the need for a practical solution for radiologists to be prepared for the daily routine with AI in the future. Then the concept is described how the application should look like to cover all requirements. Afterwards, the result of the implementation is presented. For this purpose, screenshots of developed application are shown and explanations are given how it was implemented. Subsequently, a user study of the application was also conducted to evaluate the result. The resulting outcomes are also presented. Finally, we critically reflect on whether the requirements have been met and whether the developed application serves its purpose. Furthermore, an outlook is given in which areas improvements can still be made.

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Staschewski, Tim; Kuijper, Arjan [1. Gutachten]; Appl, Stefan [2. Gutachten]

Entwicklung eines automatischen Laser-Kamera-Kalibrierungsalgorithmus auf komplexen Oberflächen

2021

Darmstadt, TU, Master Thesis, 2021

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Executing Cyclic Scientific Workflows in the Cloud

2021

Journal of Cloud Computing

We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.

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Huber, Marco; Kuijper, Arjan [1. Review]; Terhörst, Philipp [2. Review]

Explainable Face Image Quality Assessment

2021

Darmstadt, TU, Master Thesis, 2021

The high performance of today’s face recognition systems is driven by the quality of the used samples. To ensure a high quality of face images enrolled in a system, face image quality assessment is performed on these images. However, if an image is rejected due to low quality it is not obvious to the user why. Showing what led to the low quality is useful to increase trust and transparency in the system. Previous work has never addressed the explainability of their quality estimation, but only the explainability of their face recognition approaches. In this work, we propose a gradient-based method that explains which pixels contribute to the overall image quality and which do not. By adding a quality node to the end of the model, we can calculate quality-dependent gradients and visualize them. The qualitative experiments are conducted on three different datasets and we also propose a general framework for quantitative analysis of face image quality saliency maps. With our method, we can assign quality values to individual pixels and provide a meaningful explanation of how face recognition models work and how they respond to face image quality impairments. Our method provides pixel-level explanations, requires no training, applies to any face recognition model, and also takes model-specific behavior into account. By explaining how the poor quality is caused, concrete instructions can be given to people who take pictures suitable for face recognition, face image standards can be adapted, and low-quality areas can be inpainted.

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Callmer, Pia; Kuijper, Arjan [1. Review]; Noll, Matthias [2. Review]

Fluid Detection and Analysis in Ultrasonic Image Data Applying Artificial Intelligence

2021

Darmstadt, TU, Bachelor Thesis, 2021

The fast recognition and treatment of internal injuries can have an essential impact on the patient’s disease process. The application of an ultrasound device and the associated FAST protocol have been established standards used for decades for the initial detection of free fluids. Especially with ultrasound, the acquisition and evaluation of ultrasound data is very dependent on the experience of the ultrasound operator. Therefore, the automatic detection of fluids would be especially important for less experienced operators. In this thesis, the feasibility of artificial intelligence (AI) methods to automatically detect fluids in ultrasound image data is addressed. It is also evaluated how accurate the methods can distinguish between free and nonfree fluids. In particular, different variations of the state-of-the-art U-net and its effect on the results are investigated. The success of supervised learning methods is highly dependent on the design of the method, such as the architecture of the neural network, but also on the given input data. AI methods, such as the U-net, are characterized by the fact that they can be applied to different problems. Overall, the study shows that the U-net architecture provides promising results for this problem on the given data set. The potential as well as the weaknesses of the selected methods are discussed in detail in the following.

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Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-Adaption and Few-Shot Learning

2021

Pattern Recognition. ICPR International Workshops and Challenges. Proceedings, Part IV

International Conference on Pattern Recognition (ICPR) <25, 2021, Online>

Lecture Notes in Computer Science (LNCS)
12664

In previous works, a mobile application was developed using an unmodified commercial smartphone to recognize whole-body exercises. The working principle was based on the ultrasound Doppler sensing with the device built-in hardware. Applying such a lab environment trained model on realistic application variations causes a significant drop in performance, and thus decimate its applicability. The reason of the reduced performance can be manifold. It could be induced by the user, environment, and device variations in realistic scenarios. Such scenarios are often more complex and diverse, which can be challenging to anticipate in the initial training data. To study and overcome this issue, this paper presents a database with controlled and uncontrolled subsets of fitness exercises. We propose two concepts to utilize small adaption data to successfully improve model generalization in an uncontrolled environment, increasing the recognition accuracy by two to six folds compared to the baseline for different users.

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Guski, Florian; Kuijper, Arjan [1. Review]; Schufrin, Marija [2. Review]

Generalizing a Clustering of Units Visualization for Personal Cyber Analytics

2021

Darmstadt, TU, Master Thesis, 2021

Personal cyber security and privacy are topics that can often be difficult to understand for non-experts. Information Visualization is a method to support understanding data and can therefore be used in this field to make those topics more accessible. Nonetheless, the effectivity of visualizations varies greatly depending on the application context. In this thesis, a prototype that originally was designed for visualizing firewall logs was generalized to make it applicable for various personal cyber analytics use cases. To achieve that, the visualization was analyzed and reverse engineered and afterwards a generalized paradigm was designed. This paradigm was implemented and a user study was conducted to evaluate the implemented paradigm and validate it as a generalization for personal cyber analytics. The results showed that the visualization is able to support users to deal with personal cyber data and enables them to gain more knowledge. Moreover, we presented a varied set of possible application fields for the visualization.

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Gottschalk, Maximilian; Kuijper, Arjan [1. Review]; Jansen, Nils [2. Review]

Handling of Backgrounds in Furniture Recognition with Neural Networks

2021

Darmstadt, TU, Master Thesis, 2021

This work explores different object segmentation methods for the use-case of a visual furniture recommender system. The two main contributions are a novel method of synthetic furniture image generation to be used for semantic segmentation for different furniture classes and foreground-background separation. Secondly, an evaluation of the best performing segmentation network architectures in a recommender system. The synthetic dataset proves to be a suitable way to train image segmentation and to be a fitting way to represent real world data of furniture. The setup of the pipeline for generating the data provides a scalable and diverse data set through numerous augmentations. Evaluating the best performing segmentation network architectures in a visual furniture recommender system shows that significant improvements are achieved for recommendations compared to recommendations based on the recommender system without background subtraction. Recommendations, based on background subtraction, are chosen two to three times more often.

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Baumgartl, Tom; Petzold, Markus; Wunderlich, Marcel; Höhn, Markus; Archambault, Daniel; Lieser, M.; Dalpke, Alexander; Scheithauer, Simone; Marschollek, Michael; Eichel, V. M.; Mutters, Nico T.; Landesberger, Tatiana von

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

2021

IEEE Transactions on Visualization and Computer Graphics

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak – the patient zero or index patient – requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

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Krumb, Henry John; Kuijper, Arjan [1. Review]; Mukhopadhyay, Anirban [2. Review]

Learning to Track Permanent Magnets by Example

2021

Darmstadt, TU, Master Thesis, 2021

Minimally invasive surgeries require navigation modalities without the requirement for a free line-of-sight. The current gold standard for minimally invasive endovascular interventions is fluoroscopic guidance, which provides visual feedback to the interventional radiologist. To minimize radiation exposure, previous work proposes to complement fluoroscopy by Electromagnetic Tracking (EMT) as a second navigation modality. However, EMT systems require tethered sensors, which are expensive and difficult in the handling. This thesis investigates Permanent Magnet Tracking (PMT) as a potential wireless and inexpensive alternative to EMT for intra-operative navigation. Instead of locating a tethered sensor inside of the patient, a strong permanent magnet is attached to a surgical instrument and tracked by external sensors. These wired sensors on the exterior sense the magnet inside the patient, enabling wireless navigation at a low cost. The major challenge of this approach is the localization of the permanent magnet, merely based on a few simultaneous sensor readings. In this thesis, an inexpensive and reproducible PMT setup is proposed. This setup uses four magnetoresistive sensors to track a cylindrical Neodymium magnet in up to six degrees of freedom. Unlike most of its predecessors, this tracker uses a data-driven approach to localize the permanent magnet. In particular, neural networks are employed as general function approximators, which are trained to deduce magnet positions from sensor readings. Hand-collected data are fed to the neural networks to learn a mapping from magnetic field measurements to positions in 3D space, together with orientation around the x-, y- and z-axes. In an experimental phase, different optimizations for the data driven approach are proposed. These experimental results suggest that incorporating temperature data, captured by the sensors, increases prediction accuracy. Similarly, converting input points to spherical coordinates increases accuracy. However, augmenting the data by either simulated or interpolated data does not yield satisfying results.

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Linoc: A Prototyping Platform for Capacitive and Passive Electrical Field Sensing

2021

Proceedings of the 10th International Conference on Sensor Network

International Conference on Sensor Networks (SENSORNETs) <10, 2021, online>

In this paper the Linoc prototyping toolkit is presented. It is a sensor toolkit that focuses on fast prototypingof sensor systems, especially on capacitive ones. The toolkit is built around two capacitive and two ElectricPotential Sensing (EPS) groups providing unobtrusive proximity detection in the field of Human ComputerInterface (HCI). The toolkits focus lies on its usability and connectivity in order to be adapted in future researchand novel use cases. A common obstacle in the beginning of a project is the time required to familiarize withpresent tools and systems, before the actual project can be attended to. Another obstacle while tackling newtasks is the actual physical connection of sensors to the processing unit. This situation can be even worsedue to dependencies on previous work, most of the times not fully documented and missing knowledge evenif the the original designer is involved. Good toolkits can help to overcome this problem by providing alayer of abstraction and allowing to work on a higher level. If the toolkit however requires too much time tofamiliarize or behaves too restrictive, its goal has been missed and no benefits are generated. To assess thequality of the Linoc prototyping toolkit, it was evaluated in terms of three different aspects: demonstration,usage and technical performance. The usage study found good reception, a fast learning curve and an interestto use the toolkit in the future. Technical benchmarks for the capacitive sensors show a detectable range equalto its predecessors and several operational prototypes prove that the toolkit can actually be used in projects.

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Wiesmaier, Luca; Kuijper, Arjan [1. Review]; Yeste Magdaleno, Javier [2. Review]

Material Segmentation for Visual Aware Recommender Systems

2021

Darmstadt, TU, Master Thesis, 2021

People nowadays have the possibility to get recommendations for almost anything based on things they previously purchased or liked. These recommendations are often based on categories, simple colors, or other user interactions. This work presents a different approach by using precise material recognition to recommend furniture as well as clothes. These so called visual aware recommender systems are fairly unknown and have only recently gained attention. A visual aware recommender system extracts visual features from its input and uses these features to recommend accordingly. One of the biggest advantages is that these systems do not suffer from the cold start problem that many modern recommender systems have, since they do not require any other information except the visual input. In order to use material information for recommendations, precise semantic segmentation is required. Therefore, the two best performing state-of-the-art neural networks for this task are compared and evaluated, while the better model is then used in the recommender system. Performance is demonstrated by using not just one approach, but two approaches. One uses a user study to evaluate the performance gain compared to a recommender system without material recognition, and the other uses expert data known to be true to evaluate the total precision on a real live task. Both of them confirm the assumption that material recognition not only works, but also substantially improves recommendation performance especially on certain combinations.

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Zhang, Haoyu; Venkatesh, Sushma; Ramachandra, Raghavendra; Raja, Kiran; Damer, Naser; Busch, Christoph

MIPGAN - Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

2021

IEEE Transactions on Biometrics, Behavior, and Identity Science

Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach’s applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS’s vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS.

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Fellner, Dieter W. [Hrsg.]; Welling, Daniela [Red.]; Ackeren, Janine van [Red.]; Bergstedt, Bettina [Red.]; Krüger, Kathrin [Red.]; Prasche, Svenja [Betreuer]; Bornemann, Heidrun [Red.]; Roth, Anahit [Red.]

Our Year 2020

2021

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Kocon, Kevin; Kuijper, Arjan [1. Gutachten]; Bormann, Pascal [2. Gutachten]

Progressives Indexieren von massiven Punktwolken mit Big-Data-Technologien

2021

Darmstadt, TU, Master Thesis, 2021

Aufgrund der steigenden Beliebtheit von Punktwolken und der genauer werdenden LiDAR Aufnahmeverfahren wachsen die Punktwolkengrößen exponentiell. Ein wichtiger, in dieser Thesis betrachteter Anwendungsfall, ist der Aufbau eines räumlichen Index für die Web-basierte Echtzeitvisualisierung. Bereits bei aktuellen Punktwolken dauert dieser zeitintensive Prozess mit derzeitigen Verfahren bis zu mehreren Tagen. In dieser Thesis werden zwei Ansätze vorgestellt, die die Indexierungsdauer deutlich reduzieren. Zum einen wird durch die Verwendung von Big-Data-Technologien und der daraus folgenden Skalierbarkeit eine Reduktion der Gesamtindexierungsdauer erreicht. Zum anderen werden durch einen progressiven Ansatz relevante Teilbereiche der Punktwolke sukzessiv indexiert, wodurch diese bereits vor dem Verarbeiten der gesamten Punktwolke visualisiert werden können. Als Big-Data-Technologien werden Apache Spark und Apache Cassandra verwendet. Die Grundvoraussetzung für die Verwendung von Spark bietet der implementierte Top-Down Ansatz, der den Indexierungsvorgang auf das Map-Reduce Schema abbildet. Für den progressiven Ansatz werden für den Nutzer relevante Teilbereiche der Punktwolke bestimmt und nacheinander verarbeitet. Die neu eingeführte Datenstruktur des hybriden Nested-Octree Gitters ermöglicht dafür unter Anderem das sukzessive Erweitern des räumlichen Index. Die Ergebnisse zeigen, dass bei horizontaler Hardwareerweiterung eine lineare Skalierbarkeit um den Faktor 0.58 gegeben ist. Dadurch konnten massive Punktwolken im Vergleich zu lokalen Anwendungen wie dem PotreeConverter 2.0, Schwarzwald oder Entwine deutlich schneller indexiert werden. Zusätzlich ist durch den progressiven Ansatz eine Visualisierung der aktuell vom Nutzer betrachteten Teile der Punktewolke schon nach einem Bruchteil der Gesamtverarbeitungsdauer möglich.

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Grotz, Dennis; Kuijper, Arjan [1. Review]; Mukhopadhyay, Anirban [2. Review]

Registration of EMT Positions and X-Ray Images in an Aortic Phantom

2021

Darmstadt, TU, Bachelor Thesis, 2021

In this work a real-time object detection model based on the state-of-the-art architecture You Only Look Once (YOLOv3) was trained, in order to detect an Electromagnetic Tracking (EMT) sensor inside an aortic phantom. An EMT system consists of a tracking system, a field generator and an EMT sensor [4]. A problem with EMT is that magnetic fields interfere with ferromagnetic objects in the surrounding area, which corrupts the EMT positional data [12]. The object detection model can be used to detect and recalibrate the EMT sensor, in order to counteract the electromagnetic interference. This can result in a robust model to support surgeons in minimally invasive surgeries, which have advantages over open surgeries like less pain for the patients, faster recovery after operations or better cosmetic results [7]. In this work different datasets containing webcam images were collected, in order to train and test two models. One model was trained on raw webcam images, while the other one was additionally trained on augmented images. Both models were evaluated quantitatively and qualitatively for different hyperparameter configurations. They show good results on the test dataset with achieved mean Average Precision (mAP) scores of 99.89% (model without augmentations) and 99.67% (model with augmentations) and they run very fast with an interference of 26 ms and 35 Frames Per Second (FPS) on a video file. However, the model trained with augmentations generalizes better on images with different backgrounds. Furthermore, a study with some sample x-ray images showed, that the model trained with augmentations is capable of transferring from webcam images to x-ray images. Finally, this work presented a real-time object detection model for detecting an EMT sensor inside an aortic phantom with very precise detections and the capability of transferring to different domains like x-ray images.

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Stober, Gunter; Janches, Diego; Matthias, Vivien; Fritts, Dave; Marino, John; Moffat-Griffin, Tracy; Baumgarten, Kathrin; Lee, Wonseok; Murphy, Damian; Kim, Yong Ha; Mitchell, Nicholas; Palo, Scott

Seasonal Evolution of Winds, Atmospheric Tides, and Reynolds Stress omponents in the Southern Hemisphere Mesosphere–Lower Thermosphere in 2019

2021

Annales Geophysicae (ANGEO)

In this study we explore the seasonal variability of the mean winds and diurnal and semidiurnal tidal amplitude and phases, as well as the Reynolds stress components during 2019, utilizing meteor radars at six Southern Hemisphere locations ranging from midlatitudes to polar latitudes. These include Tierra del Fuego, King Edward Point on South Georgia island, King Sejong Station, Rothera, Davis, and McMurdo stations. The year 2019 was exceptional in the Southern Hemisphere, due to the occurrence of a rare minor stratospheric warming in September. Our results show a substantial longitudinal and latitudinal seasonal variability of mean winds and tides, pointing towards a wobbling and asymmetric polar vortex. Furthermore, the derived momentum fluxes and wind variances, utilizing a recently developed algorithm, reveal a characteristic seasonal pattern at each location included in this study. The longitudinal and latitudinal variability of vertical flux of zonal and meridional momentum is discussed in the context of polar vortex asymmetry, spatial and temporal variability, and the longitude and latitude dependence of the vertical propagation conditions of gravity waves. The horizontal momentum fluxes exhibit a rather consistent seasonal structure between the stations, while the wind variances indicate a clear seasonal behavior and altitude dependence, showing the largest values at higher altitudes during the hemispheric winter and two variance minima during the equinoxes. Also the hemispheric summer mesopause and the zonal wind reversal can be identified in the wind variances.

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Bauer, Christoph; Kuijper, Arjan [Betreuer]; Kohlhammer, Jörn [Betreuer]; Bergmann, Tim Alexander [Betreuer]

Semiautomatische Segmentierung von relevanten Gelenken zur Frühdiagnose von Psoriasis-Arthritis in Röntgenbildern

2021

Darmstadt, TU, Master Thesis, 2021

In dieser Arbeit wird ein dreistufiges semi-automatisches Verfahren zur Segmentierung von relevanten Gelenken für die Frühdiagnose von Psoriasis-Arthritis in Röntgenbildern vorgestellt. Psoriasis-Arthritis ist eine entzündliche Gelenkerkrankung, welche eine Verringerung des Gelenkabstandes, Gelenkfehlstellungen und bis zu Erosionen von Knochengliedern führen kann. Um dies zu detektieren werden die Knochen von Hand und Fuß segmentiert. Hierfür wird im ersten Schritt des Verfahrens das Bild vorverarbeitet. So wird mithilfe eines Medianfilters Rauschen entfernt und der Kontrast des Bildes durch Anwendung eines adaptiven Histogrammausgleichs verbessert. Für eine Basissegmentierung sorgen dann zwei adaptive Schwellwertverfahren, welche zusammengefügt werden. Für die endgültige Segmentierung wird im letzten Schritt jedes Knochenglied einzeln betrachtet und über eine Verknüpfung der Basissegmentierung mit einem erstellten Kantenbild verbessert. Somit besteht das Resultat dieses Verfahrens aus 19 Segmentierungen der einzelnen Knochenglieder, die für eine bessere Übersicht beliebig kombiniert werden können. Die Resultate werden im Anschluss mithilfe des Dice-Koeffizienten und der Hausdorff-Distanz anhand von Ground-Truth-Daten ausgewertet. Diese zeigen gerade im Bereich der Hand, mit einem durchschnittlichen Dice-Koeffizienten von 0.923 und einer durchschnittlichen Hausdorff-Distanz von 23.673, vielversprechende Ergebnisse. Im Bereich der Füße zeigen die Ergebnisse, mit einem durchschnittlichen Dice-Koeffizienten von 0.74 und einer durchschnittlichen Hausdorff-Distanz von 54.462, noch ausbaufähige Resultate.

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The Effect of Alignment on Peoples Ability to Judge Event Sequence Similarity

2021

IEEE Transactions on Visualization and Computer Graphics

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This paper describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic vs. local vs. global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98% vs. 93% correct), with the basic group getting 95% correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.

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The Effect of Alignment on People’s Ability to Judge Event Sequence Similarity

2021

IEEE Transactions on Visualization and Computer Graphics

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This paper describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic vs. local vs. global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98% vs. 93% correct), with the basic group getting 95% correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.

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Towards Combined Open Set Recognition and Out-of-Distribution Detection for Fine-grained Classification

2021

Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <16, 2021, online>

We analyze the two very similar problems of Out-of-Distribution (OOD) Detection and Open Set Recognition (OSR) in the context of fine-grained classification. Both problems are about detecting object classes that a classifier was not trained on, but while the former aims to reject invalid inputs, the latter aims to detect valid but unknown classes. Previous works on OOD detection and OSR methods are evaluated mostly on very simple datasets or datasets with large inter-class variance and perform poorly in the fine-grained setting. In our experiments, we show that object detection works well to recognize invalid inputs and techniques from the field of fine-grained classification, like individual part detection or zooming into discriminative local regions, are helpful for fine-grained OSR.

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Reiz, Achim; Albadawi, Mohamad; Sandkuhl, Kurt; Vahl, Matthias; Sidin, Dennis

Towards More Robust Fashion Recognition by Combining Deep-Learning-Based Detection with Semantic Reasoning

2021

Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)

AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) <2021, Online>

CEUR Workshop Proceedings
2846

The company FutureTV produces and distributes self-produced videos in the fashion domain. It creates revenue through the placement of relevant advertising. The placement of apposite ads, though, requires an understanding of the contents of the videos. Until now, this tagging is created manually in a labor-intensive process. We believe that image recognition technologies can significantly decrease the need for manual involvement in the tagging process. However, the tagging of videos comes with additional challenges: Preliminary, new deep-learning models need to be trained on vast amounts of data obtained in a labor-intensive data-collection process. We suggest a new approach for the combining of deep-learning-based recognition with a semantic reasoning engine. Through the explicit declaration of knowledge fitting to the fashion categories present in the training data of the recognition system, we argue that it is possible to refine the recognition results and win extra knowledge beyond what is found in the neural net.

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Boller, André; Kuijper, Arjan [1. Review]; Terhörst, Philipp [2. Review]

Towards Unsupervised Fingerprint Image Quality Assessment

2021

Darmstadt, TU, Bachelor Thesis, 2021

Fingerprint matching is one of the most popular and reliable biometric techniques used in automatic verification of a person. During the verification process of a fingerprint, the areas and in more detail the information in these areas (minutiae) of the fingerprint are compared with the corresponding information of a second fingerprint, which is called fingerprint matching. However, the quality of the fingerprint images highly affects the recognition process. Generally, the biometric sample quality is defined on its impact on a biometric recognition system, the similarity between the sample and its source, and the quality of its physical features. Poor quality fingerprints often have areas or regions that are not clear or even missing, resulting in arbitrary changes in the structure of these areas. That results in an inaccurate matching caused by comparisons of damaged areas, which leads to inaccurate matching influencing the verification results again. That is the reason why assessing the fingerprint image quality is very important because the matching performance could be significantly affected by poor quality samples. One application area that is very much affected by this problem is forensics. Forensics often deal with partial fingerprints lifted from a surface, known as latent fingerprints. Since latent fingerprints are often of poor quality, the matching performance during the recognition process is often severely impaired. Quality assessment can be used to improve biometric systems that perform automatic recognition tasks like identification or verification. Quality estimation during the enrollment process is used to ensure the best possible quality of the biometric data, thus guaranteeing a good training of the biometric systems as well as a good performance. This is a typical reason why image quality assessment is required to evaluate the quality of the images and improve the recognition process. In this thesis, two new methods are proposed to accurately assess (a) the quality of a single minutia and (b) the quality of a fingerprint. The proposed minutia quality is based on its detection reliability. A minutia is classified by randomly generated subnetworks of a minutiae classifier that determines if a minutia is a true minutia or not. The various classification results are used to determine a robustness score, considered as the detection reliability. The proposed fingerprint quality assessment method applies a stochastic method to the detection reliabilities of the minutiae to determine the quality of the fingerprint. Since this thesis addresses these two problems, both kinds of quality assessments have to be evaluated separately. The proposed minutia quality assessment method (a) is compared with Mindtct. For evaluation purposes of (b), the proposed method is compared with the current state-of-the-art quality assessment method NFIQ2 as well as its predecessor NFIQ. All experiments are evaluated on the FVC2006 database using the Bozorth3 and MCC fingerprint matchers. It can be shown that the proposed method assesses the quality on minutiae-level (a) just as good and better as Mindtct on the most experiments without the need of handcrafted quality labels. Experiments on data acquired from an electrical field sensor, for example, show that the proposed method achieves on average a 0.03 lower FNMR at a FMR of 10−2 using Bozorth3. Furthermore, the quality assessment on fingerprint-level (b) outperforms the state-of-the-art quality assessment methods NFIQ2 and NFIQ. An improvement of the recognition performance on all databases captured by real sensor types could be achieved. Observations of experimental results at a FMR of 10−2 using Bozorth3 show that the proposed method achieves a FNMR that is about 0.003 lower than the FNMR achieved by NFIQ2 on optical sensor data after rejecting the 20% worst fingerprints. Furthermore, a 0.025 lower FNMR can also be achieved on data captured by a thermal sweeping sensor.

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Hermawati, Setia; Cibulski, Lena; Lawson, Glyn

Towards Using the Critical Decision Method for Studying Visualisation-Based Decision-Making

2021

Ergonomics & Human Factors 2021. Proceedings

Ergonomics & Human Factors (CIEHF) <2021, Online>

Visualisations provide significant support for effective reasoning and decision-making processes. Its value mainly lies in its ability to turn raw data into actionable insights that lead to a decision. This requires appropriate visual representations that are designed with the decision-maker's way of reasoning in mind. Understanding the cognitive aspects underlying decision-making with visualisations is therefore crucial. Cognitive task analysis methods have been used to elicit expert knowledge in a variety of decision-making scenarios, with the Critical Decision Method (CDM) focusing on the cognitive bases in naturalistic non-routine incidents. In this study, we aim to determine the feasibility of CDM for capturing the expert knowledge, strategies, and cues involved with visualisation-based decision-making processes. Based on an analysis of four semi-structured interviews, we evaluate the method’s potential to inform the role of visualisation for human decision-making. We anticipate that our reflections on methodological insights can serve as a starting point for other human factors and visualisation researchers, who aim at studying strategies for higher-level decision-making and problem-solving tasks.

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Fellner, Dieter W. [Hrsg.]; Welling, Daniela [Red.]; Ackeren, Janine van [Red.]; Bergstedt, Bettina [Red.]; Krüger, Kathrin [Red.]; Prasche, Svenja [Betreuer]; Bornemann, Heidrun [Red.]; Roth, Anahit [Red.]

Unser Jahr 2020

2021

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Starčević, Nataša; Kuijper, Arjan [Betreuer]; Schufrin, Marija [Betreuer]

Visualizing Online Personal Data based on the Mental Models of Common Internet User Types

2021

Darmstadt, TU, Master Thesis, 2021

Der Alltag und das Leben vieler Menschen wird zunehmend von der Online Welt geprägt und beeinflusst. Doch das Internet und die Vielzahl an Online Diensten, die wir alle täglich nutzen, die uns einerseits den Alltag erleichtern können, bergen andererseits ein oftmals schwer abschätzbares Risiko, aufgrund der zunehmenden Datensammlung und der damit verbundenen Beeinträchtigung der Privatsphäre. Viele Nutzer sind sich des Ausmaßes dieser Datensammlung nicht bewusst und wissen oft nicht, welche Daten jeweils erhoben und gespeichert werden. Daher werden Techniken und Methoden entwickelt, um den Menschen bei dieser Aufgabe zu unterstützen. Jedoch sind viele Ansätze nicht an den Vorstellungen und Bedürfnissen des Nutzers ausgerichtet. Aus diesem Grund wurde im Rahmen dieser Master-Arbeit die Web-Applikation TransparencyVis zur Visualisierung personenbezogener Daten erweitert. Die bestehende Anwendung dient zur Verbesserung der Datentransparenz und erlaubt die Erkundung personenbezogener Daten von populären Online-Diensten, sogenannte DSGVO Datenexporte, die aufgrund des Auskunftsrechts des Art. 15 DSGVO von den Nutzern angefordert werden können. Auf Basis von mentalen Modellen von typischen Internetnutzern wurden Visualisierungskonzepte entworfen und prototypisch in die bestehende Anwendung implementiert. Zusätzlich wurden die Nutzer anhand ihrer Datenschutzbedenken in drei Klassen unterteilt. Mit Hilfe interaktiver Visualisierungen haben die Nutzer die Möglichkeit, ihre persönlichen DSGVO Datenexporte ausgewählter Online-Dienste zu erkunden. Im Rahmen der durchgeführten Nutzerstudie wurden die Visualisierungen auf ihre Wirksamkeit mit 33 echten Nutzern und deren persönlichen Daten evaluiert. Die Untersuchungsergebnisse lassen darauf schließen, dass die Visualisierungen das Verständnis der Nutzer in Bezug auf ihre Daten und deren Relevanz für die Privatssphäre erhöhen können. Ferner zeigen sie, dass die Integration mentaler Modelle in den Design-Prozess von Visualisierungen sich positiv auf die Erstellung nutzertyp-spezifischer Visualisierungen auswirken und deren Akzeptanz durch die Zielgruppe verbessern kann.

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Singer, Maxim; Kuijper, Arjan [1. Gutachten]; Wirtz, Andreas [2. Gutachten]

Vollautomatische Initialisierung von Coupled-Shape-Modellen mithilfe von Deep Neural Networks

2021

Darmstadt, TU, Bachelor Thesis, 2021

Die Verwendung von radiologischen Aufnahmen in der Zahnmedizin ist eine alltägliche Tätigkeit von Zahnärzten. Es geht dabei darum die Aufnahmen zu beurteilen und basierend darauf eine möglichst gute Behandlungsoption zu wählen. Zur Unterstützung und Automatisierung dieser Tätigkeit werden häufig Software-Lösungen eingesetzt. Die Benutzung von so genannten Coupled-Shape-Modellen, insbesondere in der Zahnersatz- und Zahnerhalt-Medizin, stellt die dafür benötigte technische Grundlage dar. Ein Problem von Coupled-Shape-Modellen ist, dass deren Initialisierung sehr spezifisch auf ein Modell zugeschnitten ist und ein großer Aufwand betrieben werden muss, um diese auf ein neues Modell anzupassen. In Rahmen dieser Arbeit wurde untersucht, wie Coupled-Shape-Modelle vollautomatisch und möglichst generisch initialisiert werden können. Dabei wurde die Segmentierung von GebissAufnahmen mit Hilfe von neuronalen Netzen U-Net und Mask R-CNN evaluiert. Je nach Bildtyp konnte eine Segmentierungsgenauigkeit - gemessen als Dice-Koeffizient - von über 95 % erreicht werden. Darauf aufbauend wurden zwei Methoden zur Bestimmung der Position und Skalierung des Coupled-Shape-Modells vorgestellt, diskutiert und in einem Coupled-Shape-Framework implementiert. Zur Verbesserung des Arbeitsflusses wurde eine Klassifizierung von dentalen Bildtypen evaluiert und ebenfalls in das Coupled-Shape-Framework integriert.