Bilder, die innerhalb soziologischer Forschungsarbeiten eigens produziert werden wie Fotografien oder Zeichnungen, haben meist illustrierenden oder gar nur dekorativen Charakter. Das Potential, welches innerhalb solcher visuellen Artefakte liegen kann, wird kaum reflektiert und erkannt, und zudem durch sprachbasierte traditionelle Forschungsmethoden unterdrückt. Die Dissertation rückt das Bild als Datenerhebungsmaterial in den Fokus des Forschungsprozesses. Gegenstand der Forschungsarbeit sind Zeichnungen, die themenbasiert von Interviewpartnern angefertigt werden. Bilder übernehmen die Funktion des Produktes im Interviewprozess und werden die dominierende Interviewsprache. Ziel der Arbeit ist es, das bildnerische Interview als qualitative Methode der visuellen Soziologie zu entwickeln, in welcher eigens produzierte Zeichnungen erhoben und analysiert werden.

The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments.

Among many others, one general goal of the UN sustainability strategies aims at reducing the anthropogenic land change due to land take for settlements and transport infrastructure. To monitor the success of this goal and to comprehensively study and better understand these urban dynamic processes – such as densification, growth and sprawl, or shrinkage –, quantitative measurements were introduced to assist the assessment. For the analysis of urban dynamics, the built-up area is an important measure that can be considered at different scales, one common scale being the aggregated level of urban blocks that represent a group of developed parcels bounded by topographic borders such as street lines. Regardless of the scale of quantitative analysis, however, digital spatio-temporal data are essential. While comprehensive databases exist for contemporary data, they usually lack a historic dimension. To derive these historic data about the built-up area, potential surveying methods and sources may vary. Considering the long-term characteristic of urban land change, however, topographic maps often are the only source for small-scale, spatially explicit land cover information to build a comprehensive, spatio-temporal database of built-up area, which has been demonstrated by numerous studies. However, the manual constitution of historic geographic data based on historic maps – commonly referred to as map digitization or vectorization – is a time consuming and laborious process that limits the spatial and temporal scope and, therefore, opposes comprehensive studies. Therefore, this thesis proposes an approach to automatically extract information about the built-up area at urban block level from historic topographic maps. For a number of reasons, this is a challenging task. First, topographic maps show a high degree of informational density and complexity due to their layer concept. These layers of geographic objects generally overlap leading to the (multi-)fragmentation or fusion of distinct geographic map objects. While this may not pose a challenge to a human interpreter, it does for the formalization of the computational object recognition. Second, material aging of the document as well as a poor scanning or image compression process may result in a reduced graphical quality. Third, object representations including the use of color, if present at all, show an immense diversity over space and time. To overcome these challenges in regard to cartographical image analysis, a modular process has been designed pursuing a two-step strategy: a decomposition of salient map layers is succeeded by a re-composition of the structuring map objects to delineate the built-up area at urban block level. Several experiments prove this process to achieve acceptable results with correctness values ranging from 0.97 to 0.93 for three German study maps. Behind the background of a global trend to digitize knowledge that can also be observed with historic topographic maps, the designed process represents a promising approach to efficiently prepare these historic data for integration into a spatio-temporal database of built-up area with minimal user intervention.:Declaration of Authorship Acknowledgements Summary Contents List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Scope 1.2 Challenges 1.3 Research Questions 1.4 Structure 2 Principles of Image Analysis 2.1 Human Visual Perception 2.2 Methods of Image Analyis 2.2.1 Image Segmentation 2.2.1.1 Color Image Segmentation 2.2.1.2 Texture-based Segmentation 2.2.1.3 Morphology-based Segmentation 2.2.1.4 Further Segmentation Approaches 2.2.2 Object/Pattern Recognition 2.2.2.1 Strategies in Pattern Recognition 2.2.2.2 Approaches in Pattern Recognition 2.2.3 Object Reconstruction 2.2.3.1 Reconstruction of Contours 2.2.3.2 Raster-vector Conversion 2.3 Summary 3 Cartographic Image Analysis 3.1 Geoinformation from Cartographic Raster Maps 3.1.1 Raster Maps 3.1.2 Research History 3.1.3 Research – State of the Art 3.1.3.1 Separation of Raster Layers based on Color 3.1.3.2 Extraction and Recognition of Map Objects 3.1.3.3 Automated Georeferencing 3.1.4 Delineation of Built-up Area from Cartographic Raster Maps 3.2 Further Sources for the Delineation of Built-up Area 3.3 Summary and Interim Conclusions 4 Concept and Methodology 4.1 Concept - Preliminary Considerations 4.1.1 Defining the Subject of Delineatoin – the Urban Block 4.1.2 Data Characteristics 4.1.3 Cartographical Representation and Higher-Level Demarcation of Built-up Area 4.2 Methodological Design 4.2.1 Requirements to the Process and the Input Data 4.2.2 General Methodical Approach 4.2.3 Derivation of the General Delineation Process 4.2.4 Module Map Objects 4.2.4.1 Building Symbols 4.2.4.2 Residential Area Hatching 4.2.4.3 Railroads and Tramlines 4.2.5 Module Street Block Delineation 4.2.5.1 Street Network 4.2.5.2 Reconstruction of Street Block Objects 4.2.5.3 Evaluation of Street Block Objects 4.2.6 Delineation of Built-up Area 4.2.6.1 Module Building Grouping 4.2.6.2 Module Built-up Area 4.3 Implementation 5 Evaluation and Discussions 5.1 Evaluation Frameset 5.1.1 Study Maps 5.1.2 Reference Data 5.1.3 Methodology 5.2 Experiments and Results 5.2.1 Experiments 5.2.1.1 E.0 – Delineate Built-up Area Using the General Process 5.2.1.2 E.1 – Delineate Built-up Area Using a Deviation of the General Process 5.2.1.3 E.2 – Delineate Built-up Area Using Maps with Varying Spatial Resolution 5.2.2 Results 5.2.2.1 R.0 – Delineation Results of the General Process 5.2.2.2 R.1 – Delineation Results of the Deviated Process Variants 5.2.2.3 R.2 – Delineation Results of the Deviated Map Resolution Variants 5.3 Discussions 5.3.1 Strengths and Limitations 5.3.2 Comparision of Delineation Results to other Studies 5.3.3 Applications and Transferability to other Maps 6 Conclusion and Outlook 6.1 Revising the Research Questions 6.2 Scientific Contribution 6.3 Future Research Perspectives References Appendix A.1 List of Process Parameters and their Application A.2 Exemplary Delineation Results

In dieser Arbeit wird eine neue Methode der Vitalitätsbestimmung von Cryptosporidium parvum-Oozysten beschrieben. Die gereinigten Oozysten wurden in einer HCT-8-zelllinie kultiviert und mittels IFAT ausgewertet. Um eine genaue Quantifizierung der fluoreszierenden Flächen vornehmen zu können, wurden die Bilder einer Bildanalysesoftware zugeführt und analysiert. Die Menge eingesäter Oozysten korrelierte signifikant mit den gemessenen Flächen intrazellulärer Entwicklungsstadien. In diesem System wurden verschiedene Feldisolate vergleichend getestet sowie die Vitalität thermisch inaktivierter Oozysten bestimmt.

Fluorescence microscopy is an invaluable technique for studying the intricate process of organism development. The acquisition process, however, is associated with the fundamental trade-off between the quality and reliability of the acquired data. On one hand, the goal of capturing the development in its entirety, often times across multiple spatial and temporal scales, requires extended acquisition periods. On the other hand, high doses of light required for such experiments are harmful for living samples and can introduce non-physiological artifacts in the normal course of development. Conventionally, a single set of acquisition parameters is chosen in the beginning of the acquisition and constitutes the experimenter’s best guess of the overall optimal configuration within the aforementioned trade-off. In the paradigm of adaptive microscopy, in turn, one aims at achieving more efficient photon budget distribution by dynamically adjusting the acquisition parameters to the changing properties of the sample. In this thesis, I explore the principles of adaptive microscopy and propose a range of improvements for two real imaging scenarios. Chapter 2 summarizes the design and implementation of an adaptive pipeline for efficient observation of the asymmetrically dividing neurogenic progenitors in Zebrafish retina. In the described approach the fast and expensive acquisition mode is automatically activated only when the mitotic cells are present in the field of view. The method illustrates the benefits of the adaptive acquisition in the common scenario of the individual events of interest being sparsely distributed throughout the duration of the acquisition. Chapter 3 focuses on computational aspects of segmentation-based adaptive schemes for efficient acquisition of the developing Drosophila pupal wing. Fast sample segmentation is shown to provide a valuable output for the accurate evaluation of the sample morphology and dynamics in real time. This knowledge proves instrumental for adjusting the acquisition parameters to the current properties of the sample and reducing the required photon budget with minimal effects to the quality of the acquired data. Chapter 4 addresses the generation of synthetic training data for learning-based methods in bioimage analysis, making them more practical and accessible for smart microscopy pipelines. State-of-the-art deep learning models trained exclusively on the generated synthetic data are shown to yield powerful predictions when applied to the real microscopy images. In the end, in-depth evaluation of the segmentation quality of both real and synthetic data-based models illustrates the important practical aspects of the approach and outlines the directions for further research.

Understanding the development, organization, and function of tissues is a central goal in developmental biology. With modern time-lapse microscopy, it is now possible to image entire tissues during development and thereby localize subcellular proteins. A particularly productive area of research is the study of single layer epithelial tissues, which can be simply described as a 2D manifold. For example, the apical band of cell adhesions in epithelial cell layers actually forms a 2D manifold within the tissue and provides a 2D outline of each cell. The Drosophila melanogaster wing has become an important model system, because its 2D cell organization has the potential to reveal mechanisms that create the final fly wing shape. Other examples include structures that naturally localize at the surface of the tissue, such as the ciliary components of planarians. Data from these time-lapse movies typically consists of mosaics of overlapping 3D stacks. This is necessary because the surface of interest exceeds the field of view of todays microscopes. To quantify cellular tissue dynamics, these mosaics need to be processed in three main steps: (a) Extracting, correcting, and stitching individ- ual stacks into a single, seamless 2D projection per time point, (b) obtaining cell characteristics that occur at individual time points, and (c) determine cell dynamics over time. It is therefore necessary that the applied methods are capable of handling large amounts of data efficiently, while still producing accurate results. This task is made especially difficult by the low signal to noise ratios that are typical in live-cell imaging. In this PhD thesis, I develop algorithms that cover all three processing tasks men- tioned above and apply them in the analysis of polarity and tissue dynamics in large epithelial cell layers, namely the Drosophila wing and the planarian epithelium. First, I introduce an efficient pipeline that preprocesses raw image mosaics. This pipeline accurately extracts the stained surface of interest from each raw image stack and projects it onto a single 2D plane. It then corrects uneven illumination, aligns all mosaic planes, and adjusts brightness and contrast before finally stitching the processed images together. This preprocessing does not only significantly reduce the data quantity, but also simplifies downstream data analyses. Here, I apply this pipeline to datasets of the developing fly wing as well as a planarian epithelium. I additionally address the problem of determining cell polarities in chemically fixed samples of planarians. Here, I introduce a method that automatically estimates cell polarities by computing the orientation of rootlets in motile cilia. With this technique one can for the first time routinely measure and visualize how tissue polarities are established and maintained in entire planarian epithelia. Finally, I analyze cell migration patterns in the entire developing wing tissue in Drosophila. At each time point, cells are segmented using a progressive merging ap- proach with merging criteria that take typical cell shape characteristics into account. The method enforces biologically relevant constraints to improve the quality of the resulting segmentations. For cases where a full cell tracking is desired, I introduce a pipeline using a tracking-by-assignment approach. This allows me to link cells over time while considering critical events such as cell divisions or cell death. This work presents a very accurate large-scale cell tracking pipeline and opens up many avenues for further study including several in-vivo perturbation experiments as well as biophysical modeling. The methods introduced in this thesis are examples for computational pipelines that catalyze biological insights by enabling the quantification of tissue scale phenomena and dynamics. I provide not only detailed descriptions of the methods, but also show how they perform on concrete biological research projects.

Zur detaillierten Beschreibung des Lastabtragverhaltens textiler Bewehrung im Beton ist es erforderlich, das Penetrationsverhalten der Betonmatrix in die stark heterogene Garnstruktur zu beschreiben. Zur Charakterisierung der Mikrostruktur im Querschnitt wurde eine Bildanalysemethode entwickelt, um die Verbundsituation der einzelnen Filamente quantitativ auswerten zu können. Um eine räumliche Beschreibung der Verbundsituation zu erreichen, wurde die Strategie verfolgt, aus aufeinander folgenden Schichtaufnahmen mittels Rasterelektronenmikroskopie eine räumliche Struktur abzuleiten. Hierzu wurden zum einen die experimentelle Vorgehensweise erarbeitet und zum anderen ein Ansatz für die Zuordnung der Filamente zwischen den einzelnen Querschnitten entwickelt.

The centrosome is a dynamic organelle found in all animal cells that serves as a microtubule organizing center during cell division. Most of the centrosome components have been identified by genetic screens over the last decade, but little is known about how these components interact with each other to form a functional centrosome. Towards a better understanding of the molecular organization of the centrosome, we investigated the mechanism that regulates the size of the centrosome in the early C. elegans embryo. For this, we monitored fluorescently labeled centrosomes in living embryos and developed a suite of image analysis algorithms to quantify the centrosomes in the resulting 3D time-lapse images. In particular, we developed a novel algorithm involving a two-stage linking process for tracking entrosomes, which is a multi-object tracking task. This fully automated analysis pipeline enabled us to acquire time-resolved data of centrosome growth in a large number of embryos and could detect subtle phenotypes that were missed by previous assays based on manual image analysis. In a first set of experiments, we quantified centrosome size over development in wild-type embryos and made three essential observations. First, centrosome volume scales proportionately with cell volume. Second, beginning at the 4-cell stage, when cells are small, centrosome size plateaus during the cell cycle. Third, the total centrosome volume the embryo gives rise to in any one cell stage is approximately constant. Based on our observations, we propose a ‘limiting component’ model in which centrosome size is limited by the amounts of maternally derived centrosome components. In a second set of experiments, we tested our hypothesis by varying cell size, centrosome number and microtubule-mediated pulling forces. We then manipulated the amounts of several centrosomal proteins and found that the conserved centriolar and pericentriolar material protein SPD-2 is one such component that determines centrosome size.

Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces. / Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln.

Zugl.: Karlsruhe, Univ., Diss., 2005