Cherenkov-Teleskope sind in der Lage, das schwache Cherenkovlicht aus Teilchenschauern zu detektieren, die von kosmischen Teilchen mit Energien von ca. 100 GeV bis 100 TeV in der Erdatmosphäre initiiert werden. Das Ziel ist die Detektion von Cherenkovlicht aus Schauern, die von Gammastrahlen erzeugt wurden, der größte Teil der Schauer stammt jedoch von geladenen Teilchen. Im Jahr 2012 wurde das H.E.S.S.-Observatorium in Namibia, bis dahin bestehend aus vier Teleskopen mit 100 m²-Spiegeln, um ein fünftes Teleskop mit einer Spiegelfläche von ca. 600 m² ergänzt. Aufgrund der großen Spiegelfläche besitzt dieses Teleskop die niedrigste Energieschwelle aller Teleskope dieser Art. In dieser Dissertation wird ein schneller Algorithmus namens MonoReco präsentiert, der grundlegende Eigenschaften der Gammastrahlen wie ihre Energien und Richtungen rekonstruieren kann. Dieser Algorithmus kann weiterhin unterscheiden, ob Schauer von Gammastrahlen oder von geladenen Teilchen der kosmischen Strahlung initiiert wurden. Diese Aufgaben werden mit mithilfe von künstlichen neuronalen Netzwerken erfüllt, welche ausschließlich die Momente der Intensitätsverteilungen in der Kamera des neuen Teleskops analysieren. Eine Energieschwelle von 59 GeV und Richtungsauflösungen von 0.1°-0.3° werden erreicht. Das Energiebias liegt bei wenigen Prozent, die Energieauflösung bei 20-30%. Unter anderem mit dem MonoReco-Algorithmus wurden Daten, die in der Zeit um das Periastron des Binärsystems PSR B1259-63/LS 2883 im Jahre 2014 genommen wurden, analysiert. Es handelt sich hierbei um einen Neutronenstern, der sich in einem 3,4-Jahres-Orbit um einen massereichen Stern mit einer den Stern umgebenden Scheibe aus Gas und Plasmen befindet. Zum ersten Mal konnte H.E.S.S. das Gammastrahlenspektrum dieses Systems bei Energien unterhalb von 200 GeV messen. Weiterhin wurde bei erstmaligen Beobachtungen zur Zeit des Periastrons ein lokales Flussminimum gemessen. Sowohl vor dem ersten als auch nach dem zweiten Transit des Neutronensterns durch die Scheibe wurden hohe Flüsse gemessen. Im zweiten Fall wurden Beobachtungen erstmals zeitgleich mit dem Fermi-LAT-Experiment durchgeführt, das wiederholt sehr hohe Flüsse in diesem Teil des Orbits messen konnte. Ein Vergleich der gemessenen Flüsse mit Vorhersagen eines leptonischen Modells zeigt gute Übereinstimmungen. / Cherenkov telescopes can detect the faint Cherenkov light emitted by air showers that were initiated by cosmic particles with energies between approximately 100 GeV and 100 TeV in the Earth's atmosphere. Aiming for the detection of Cherenkov light emitted by gamma ray-initiated air showers, the vast majority of all detected showers are initiated by charged cosmic rays. In 2012 the H.E.S.S. observatory, until then comprising four telescopes with 100 m² mirrors each, was extended by adding a much larger fifth telescope with a very large mirror area of 600 m². Due to the large mirror area, this telescope has the lowest energy threshold of all telescopes of this kind. In this dissertation, a fast algorithm called MonoReco is presented that can reconstruct fundamental properties of the primary gamma rays like their direction or their energy. Furthermore, this algorithm can distinguish between air showers initiated either by gamma rays or by charged cosmic rays. Those tasks are accomplished with the help of artificial neural networks, which analyse moments of the intensity distributions in the camera of the new telescope exclusively. The energy threshold is 59 GeV and angular resolutions of 0.1°-0.3° are achieved. The energy reconstruction bias is at the level of a few percent, the energy resolution is at the level of 20-30%. Data taken around the 2014 periastron passage of the gamma-ray binary PSR B1259-63/LS 2883 were analysed with, among others, the MonoReco algorithm. This binary system comprises a neutron star in a 3.4 year orbit around a massive star with a circumstellar disk consisting of gas and plasma. For the first time the gamma-ray spectrum of this system could be measured by H.E.S.S. down to below 200 GeV. Furthermore, a local flux minimum could be measured during unprecedented measurements at the time of periastron. High fluxes were measured both before the first and after the second transit of the neutron star through the disk. In the second case measurements could be performed for the first time contemporaneously with the Fermi-LAT experiment, which has repeatedly detected very high fluxes at this part of the orbit. A good agreement between measured fluxes and predictions of a leptonic model is found.

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Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools.

Inhalt: Nachhaltiger Stadtumbau und Erneuerbare Energien - Ansatz und Konzepte einer ökologischen Stadtentwicklung Wohnprojekt Dresden Pillnitz - Innovatives Energiekonzept für zwei Mehrfamilienhäuser in Passivhausbauweise Tiefbautechnik in der Praxis - Die spektakuläre Gablenzbach-Sanierung Mauern für die Ewigkeit - Rekonstruktion, Sanierung und Neubau von Mauerwerksbauten Wärmebrückenbeheizung - Ein Widerspruch? Solares Heizen mit Großanlagen - Chancen und Perspektiven Bauphysikalische Wertung von belüfteten und nicht belüfteten Dächern Kritische Aspekte zum sommerlichen Wärmeschutz – Vorbemessung

Virtual reconstruction of historic sites, planning of restorations and attachments of new building parts, as well as forest inventory are few examples of fields that benefit from the application of 3D surveying data. Originally using 2D photo based documentation and manual distance measurements, the 3D information obtained from multi camera and laser scanning systems realizes a noticeable improvement regarding the surveying times and the amount of generated 3D information. The 3D data allows a detailed post processing and better visualization of all relevant spatial information. Yet, for the extraction of the required information from the raw scan data and for the generation of useable visual output, time-consuming, complex user-based data processing is still required, using the commercially available 3D software tools. In this context, the automatic object recognition from 3D point cloud and depth data has been discussed in many different works. The developed tools and methods however, usually only focus on a certain kind of object or the detection of learned invariant surface shapes. Although the resulting methods are applicable for certain practices of data segmentation, they are not necessarily suitable for arbitrary tasks due to the varying requirements of the different fields of research. This thesis presents a more widespread solution for automatic scene reconstruction from 3D point clouds, targeting street scenarios, specifically for the task of traffic accident scene analysis and documentation. The data, obtained by sampling the scene using a mobile scanning system is evaluated, segmented, and finally used to generate detailed 3D information of the scanned environment. To realize this aim, this work adapts and validates various existing approaches on laser scan segmentation regarding the application on accident relevant scene information, including road surfaces and markings, vehicles, walls, trees and other salient objects. The approaches are therefore evaluated regarding their suitability and limitations for the given tasks, as well as for possibilities concerning the combined application together with other procedures. The obtained knowledge is used for the development of new algorithms and procedures to allow a satisfying segmentation and reconstruction of the scene, corresponding to the available sampling densities and precisions. Besides the segmentation of the point cloud data, this thesis presents different visualization and reconstruction methods to achieve a wider range of possible applications of the developed system for data export and utilization in different third party software tools.

Cette thèse relate la conception d’une entreprise artiste créée par l’auteur, artiste entrepreneuse, dans l’intention de témoigner des mutations des villes et des choix effectués en matière de conservation ou de destruction. Urban Memory, entreprise de « rechargement en contenu mnémonique », a vu le jour en 2012 à Dresde, capitale saxonne, bombardée en 1945. Située à l’est de l’Allemagne, cette ville est en constant chantier depuis des décennies. C’est par l’intermédiaire d’une pratique artistique mêlant la peinture et la sérigraphie avec des moyens de communication mis en scène dans l’urbain qu’Urban Memory entend rendre compte des transformations du paysage architectural et accompagner les débats autour de l’identité et la mémoire de la ville. Dans une première partie, l’auteur retrace son parcours de découverte et de réflexion sur Dresde au travers de premiers travaux ; présente le contexte historique, politique et économique des mutations ; puis nous livre une analyse des entreprises artistes à partir de laquelle elle a conçu Urban Memory, son mode de fonctionnement et ses stratégies. Dans une seconde partie, l’auteur nous guide dans le centre historique de Dresde, le long de ses monuments et mémoriaux, afin de souligner tantôt l’éloquence, tantôt le silence dont sont capables de tels édifices et installations dans la ville ; puis se tourne vers la scène artistique contemporaine et ses moyens de création d’une mémoire « vivante ». Enfin, après avoir mis en lien ses propres outils artistiques et le contexte de sa recherche, l’auteur nous révèle les premières réalisations et les projets d’installation in situ de son entreprise. / This thesis examines the author’s conceptualization and establishment of an artist company with the objective of tracing urban transformations and of assessing decisions to conserve or destroy parts of the city. Urban Memory, a company that has as its goal the “recharging of mnemonic content,” was founded in Saxony’s capital city Dresden in 2012. Located in the east of Germany, this city, bombarded in 1945, has constantly been under construction in the past decades. Urban Memory takes account of the architectural landscape’s transformations and accompanies debates about the identity and memory of the city. It does so by means of a hybrid form of artistic practices—painting and serigraphy—and communication media placed in the urban context. The first section of this thesis explores the artist’s discovery and initial reflections on Dresden in the context of her first works; it explores the historical, political and economic context of the city’s transformations; and it analyzes artist companies based on which Urban Memory has been conceived as well as its basic functional framework and strategic outlook. The second part zooms in on the historical center of Dresden, its monuments and memorial sites, so as to expose both the eloquence and silence with which the built environment of the city treats its past. Moreover, this part turns to the milieu of contemporary artists and their means of creating a ‘living’ memory. Finally, after having discussed and related the artistic dimension of her project, the author presents the company’s first projects and in situ installations.

Applicable image-based reconstruction of three-dimensional (3D) objects offers many interesting industrial as well as private use cases, such as augmented reality, reverse engineering, 3D printing and simulation tasks. Unfortunately, image-based 3D reconstruction is not yet applicable to these quite complex tasks, since the resulting 3D models are single, monolithic objects without any division into logical or functional subparts. This thesis aims at making image-based 3D reconstruction feasible such that captures of standard cameras can be used for creating functional 3D models. The research presented in the following does not focus on the fine-tuning of algorithms to achieve minor improvements, but evaluates the entire processing pipeline of image-based 3D reconstruction and tries to contribute at four critical points, where significant improvement can be achieved by advanced human-computer interaction: (i) As the starting point of any 3D reconstruction process, the object of interest (OOI) that should be reconstructed needs to be annotated. For this task, novel pixel-accurate OOI annotation as an interactive process is presented, and an appropriate software solution is released. (ii) To improve the interactive annotation process, traditional interface devices, like mouse and keyboard, are supplemented with human sensory data to achieve closer user interaction. (iii) In practice, a major obstacle is the so far missing standard for file formats for annotation, which leads to numerous proprietary solutions. Therefore, a uniform standard file format is implemented and used for prototyping the first gaze-improved computer vision algorithms. As a sideline of this research, analogies between the close interaction of humans and computer vision systems and 3D perception are identified and evaluated. (iv) Finally, to reduce the processing time of the underlying algorithms used for 3D reconstruction, the ability of artificial neural networks to reconstruct 3D models of unknown OOIs is investigated. Summarizing, the gained improvements show that applicable image-based 3D reconstruction is within reach but nowadays only feasible by supporting human-computer interaction. Two software solutions, one for visual video analytics and one for spare part reconstruction are implemented. In the future, automated 3D reconstruction that produces functional 3D models can be reached only when algorithms become capable of acquiring semantic knowledge. Until then, the world knowledge provided to the 3D reconstruction pipeline by human computer interaction is indispensable.

Die Positronen-Emissions-Tomographie (PET) bei der Schwerionentherapie ist eine wichtige Methode zur Qualitätskontrolle in der Tumortherapie mit Kohlenstoffionen. Die vorliegende Arbeit beschreibt die Verbesserungen des PET-Verfahrens, wodurch sich in der Folge präzisere Aussagen zur Dosisapplikation treffen lassen. Aufbauend auf den Grundlagen (Kap. 2) werden die Neuentwicklungen in den drei darauf folgenden Abschnitten (Modellierung des Abbildungsprozesses bei der PET, Streukorrektur für PET bei der Schwerionentherapie, Verarbeitung der rekonstruierten PET-Daten) beschrieben. Die PET-Methode bei der Schwerionentherapie basiert auf dem Vergleich zwischen den gemessenen und vorausberechneten Aktivitätsverteilungen. Die verwendeten Modelle in der Simulation (Erzeugung der Positronenemitter, deren Ausbreitung, der Transport und der Nachweis der Annihilationsquanten) sollten so präzise wie möglich sein, damit ein aussagekräftiger Vergleich möglich wird. Die Genauigkeit der Beschreibung der physikalischen Prozesse wurde verbessert und zeiteffiziente Algorithmen angewendet, die zu einer erheblichen Verkürzung der Rechenzeit führen. Die erwarteten bzw. die gemessenen räumlichen Radioaktivitätsverteilungen werden mit einem iterativen Verfahren rekonstruiert [Lau99]. Die gemessenen Daten müssen hinsichtlich der im Messobjekt auftretenden Comptonstreuung der Annihilationsphotonen korrigiert werden. Es wird ein geeignetes Verfahren zur Streukorrektur für die Therapieüberwachung vorgeschlagen und dessen Realisierung beschrieben. Zur Einschätzung der Güte der Behandlung wird die gemessene und die simulierte Aktivitätsverteilung verglichen. Dazu wurde im Rahmen der vorliegenden Arbeit eine Software entwickelt, das die rekonstruierten PET-Daten visualisiert und die anatomischen Informationen des Röntgentomogramms mit einbezieht. Nur durch dieses Auswerteverfahren war es möglich, Fehler im physikalischen Strahlmodell aufzudecken und somit die Bestrahlungsplanung zu verbessern.