In this work has been examined, how the existing model of the simulation of cables and hoses can be advanced. Therefore an investigation has been made on the main influences to the shape simulation and the factors of constraints and side conditions were analyzed. For the validation of the accuracy, the simulation has to be compared to real specimen behavior. To obtain a very precise digitalization of the shape, the choice was made to use a laser scanner that converts the pointcloud into a .vrml file which can be imported into the simulation environment. The assumption was that the simulation method itself has the highest impact to the simulated shape. This is why the capabilities of the most sophisticated methods have been analyzed. The main criterion for the success of a simulation approach proved not to be accuracy, as expected. Process integration and usability showed to be of higher interest for the efficient exertion. Other factors like the pricing, the functionality and the real-time capability were assayed as well. The analyzed methods are based on the solution of the equations of elasticity with different ways of discetization, finite-elements and a spring-impulse-system. Since the finite-element-system takes several minutes for the computation of the shape and the spring-impulse-system reacts retarded on user manipulation, the competitiveness of these approaches is low. The other methods distinguish more in real-time performance, data interfaces and functionality than in accuracy. For the accuracy of a system, the consideration of other factors proved to be very important. As one of these main factors, the accurate assignment of the material properties was indicated. Until the start of this work, only the finite-element-approach dealt with this factor, but no documentation or validation is provided. In the publications of the other methods, the material properties are estimated to obtain a plausible simulation shape. Therefore the specific material values of real specimen have been measured and assigned to the simulation. With the comparison to the real shape it has been proven that the accuracy is very high with the measured properties. Since these measurements are very costly and time consuming, an investigation on a faster and cheaper way to obtain these values has been made. It has been assumed that with the knowledge of the cross-section it should be possible to compute the specimen behavior. Since the braid distribution changes individually from specimen to specimen, a more general way to obtain the values needed to be found. The program composer has been developed, where only the number of the different braids and the taping is entered. It computes with very high precision the stiffness, the density and the final diameter of the bundle. With the measured values and the fitting to the real shape it has been proven that the simulation approach reflects the precise behavior of cables and hoses. Since the stiffness of the single braids is wasteful to measure, a measurement setup was created where the stiffness has a large impact to the shape. With known density, the stiffness of the specimen can be reconstructed precisely. Thus a fast and beneficial way of obtaining the stiffness of a cable has been invented. The poissons ratio of cables and bundles cannot be measured with a tensile test, since the inner structure is very complex. For hoses, the variation of the inner diameter has been measured during the tensile test as well. The resulting values were reasonable, but their accuracy could not be proven. For cables and hoses, it has been tried to obtain the poissons ratio via the computation of the cross section, but the influence of individual changes and the crosstalk of the braids is very high. Therefore a setup was constructed where the torsion stiffness can be measured. For cables and hoses, the individual cross-sections and taping lead to varying results. For hoses, expected and repeatable good values for the poissons ratio were obtained. The low influence of the poisons ratio in the range between 0 and 0.5 has been proven. Therefore we decided to follow the advice of [Old06] and our own experiences to set the poisons ratio for cables and bundles to 0.25. With the knowledge of the measurability and the capabilities of the developed program composer, a procedure to obtain material properties for bundles has been designed. 1. Measurement of the braid density with via pyknometer or mass, length and diameter. 2. Empirical reconstruction of the stiffness with the designed setup. 3. Composing the bundle with the program composer. 4. Adding a factor for the taping and transfer the values to the simulation. The model of the cable simulation has been improved as follows: The main influences in the simulation of cables and hoses are the simulation method, the material properties and the geometric constraints. To obtain higher accuracy, an investigation on the correct material properties is indispensable. The scientific determination of material properties for the simulation of cables, bundles and hoses has been performed for the first time. The influence of geometrical constraints has been analyzed and documented. The next steps are the analysis of pre-deformation and further investigations to the determination of the poisons ratio with a more precise torsion test. All analysis were made with the simulation approach fleXengine. A comparison to other simulation methods would be of high interest.

Will Eisners Graphic Novels zeugen von einer tiefgehenden Identitifation mit dem Judentum als Volkszugehörigkeit, Religion und Kultur und spiegeln das Judentum in all seinen Facetten wider. Dabei ist besonders hervorzuheben, dass die Entwicklung des Gesamtwerks parallel verläuft zur Geschichte der Emanzipation der jüdischen Bevölkerung in New York City. Der Band clustert die jüdischen Aspekte in Eisners Werk in beispielsweise Faktoren kollektiver Erinnerung, Umgang mit und Kampf gegen Antisemitismus und religiöse Besonderheiten.

Die Forschung im Bereich der modellbasierten Objekterkennung und Objektlokalisierung hat eine vielversprechende Zukunft, insbesondere die Gebäudeerkennung bietet vielfaltige Anwendungsmöglichkeiten. Die Bestimmung der Position und der Orientierung des Beobachters relativ zu einem Gebäude ist ein zentraler Bestandteil der Gebäudeerkennung. Kern dieser Arbeit ist es, ein System zur modellbasierten Poseschätzung zu entwickeln, das unabhängig von der Anwendungsdomäne agiert. Als Anwendungsdomäne wird die modellbasierte Poseschätzung bei Gebäudeaufnahmen gewählt. Vorbereitend für die Poseschätzung bei Gebäudeaufnahmen wird die modellbasierte Erkennung von Dominosteinen und Pokerkarten realisiert. Eine anwendungsunabhängige Kontrollstrategie interpretiert anwendungsspezifische Modelle, um diese im Bild sowohl zu lokalisieren als auch die Pose mit Hilfe dieser Modelle zu bestimmen. Es wird explizit repräsentiertes Modellwissen verwendet, sodass Modellbestandteilen Bildmerkmale zugeordnet werden können. Diese Korrespondenzen ermöglichen die Kamerapose aus einer monokularen Aufnahme zurückzugewinnen. Das Verfahren ist unabhängig vom Anwendungsfall und kann auch mit Modellen anderer rigider Objekte umgehen, falls diese der definierten Modellrepräsentation entsprechen. Die Bestimmung der Pose eines Modells aus einem einzigen Bild, das Störungen und Verdeckungen aufweisen kann, erfordert einen systematischen Vergleich des Modells mit Bilddaten. Quantitative und qualitative Evaluationen belegen die Genauigkeit der bestimmten Gebäudeposen. In dieser Arbeit wird zudem ein halbautomatisches Verfahren zur Generierung eines Gebäudemodells vorgestellt. Das verwendete Gebäudemodell, das sowohl semantisches als auch geometrisches Wissen beinhaltet, den Aufgaben der Objekterkennung und Poseschätzung genügt und sich dennoch an den bestehenden Normen orientiert, ist Voraussetzung für das Poseschätzverfahren. Leitgedanke der Repräsentationsform des Modells ist, dass sie für Menschen interpretierbar bleibt. Es wurde ein halbautomatischer Ansatz gewählt, da die automatische Umsetzung dieses Verfahrens schwer die nötige Präzision erzielen kann. Das entwickelte Verfahren erreicht zum einen die nötige Präzision zur Poseschätzung und reduziert zum anderen die Nutzerinteraktionen auf ein Minimum. Eine qualitative Evaluation belegt die erzielte Präzision bei der Generierung des Gebäudemodells.

This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.

Climate change is an existential threat to human survival, the social organization of society, and the stability of ecosystems. It is thereby profoundly frightening. In the face of threat, people often want to protect themselves instead of engaging in mitigating behaviors. When psychological resources are insufficient to cope, people often respond with different forms of denial. In this dissertation, I contribute original knowledge to the understanding of the multifaceted phenomenon of climate denial from a psychological perspective. There are four major gaps in the literature on climate denial: First, the spectrum of climate denial as a self-protective response to the climate crisis has not received attention within psychology. Second, basic psychological need satisfaction, a fundamental indicator of human functioning and the ability to cope with threat, has not been investigated as a predictor of climate denial. Third, relations of the spectrum of climate denial to climate-relevant emotions, specifically climate anxiety, have not been examined empirically. Forth, it has not been investigated how the spectrum of climate denial relates to established predictors of climate denial, namely right-wing ideological convictions and male gender. To address those gaps, I investigate what the spectrum of climate denial looks like in the German context and how it relates to basic psychological need satisfaction and frustration, pro-environmental behavior, climate anxiety, ideological conviction, and gender. Five manuscripts reveal that climate denial exists on a spectrum in the German context, ranging from the distortion of facts (interpretive climate denial, specifically denial of personal and global outcome severity) to the denial of the implications of climate change (implicatory climate denial, specifically avoidance, denial of guilt, and rationalization of one's own involvement). Across analyses, low basic psychological need satisfaction predicted the spectrum of climate denial, which was negatively related to pro-environmental behavior. Climate denial was generally negatively related to climate anxiety, except for a positive association of avoidance and climate anxiety. Right-wing ideological conviction was the strongest predictor of climate denial across the spectrum. However, low need satisfaction and male gender were additional weaker predictors of implicatory climate denial. These findings suggest that the spectrum of climate denial serves many psychological functions. Climate denial is possibly both a self-protective strategy to downregulate emotions and to protect oneself from loss of privilege. In short, it represents a barrier to climate action that may only be resolved once people have sufficient psychological resources to face the threat of climate change and cope with their underlying self-protective, emotional responses.