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Augmented Reality ist eine Technologie, mit der die Wahrnehmung der realen Umgebung durch computergenerierte Sinnesreize verändert bzw. erweitert wird. Zur Erweiterung dieser „angereicherten Realität“ werden virtuelle Informationen wie z.B. 3D-Objekte, Grafiken und Videos in Echtzeit in Abbildern der realen Umgebung dargestellt. Die Erweiterungen helfen dem Anwender Aufgaben in der Realität auszuführen, da sie ihm Informationen bereitstellen, die er – ohne AR – nicht unmittelbar wahrnehmen könnte. Die Zielsetzung ist, dem Benutzer den Eindruck zu vermitteln, dass die reale Umgebung und die virtuellen Objekte koexistent miteinander verschmelzen. Für AR-Anwendungen existieren zahlreiche potenzielle Einsatzgebiete, doch verhindern bisher einige Probleme die Verbreitung dieser Technologie. Einer breiten Nutzung von AR-Anwendungen steht beispielsweise die Problematik gegenüber, dass deren Erstellung hohe programmiertechnische Anforderungen an die Entwickler stellt. Zur Verminderung dieser Probleme ist es wünschenswert Benutzern ohne Programmierkenntnisse (Autoren) die Entwicklung von AR-Anwendungen zu ermöglichen. Zum anderen bestehen technologische Probleme bei den für die Registrierung der virtuellen Objekte essenziellen Trackingverfahren. Weiterhin weisen die bisherigen AR-Anwendungen im Allgemeinen und die mittels autorenorientierter Systeme erstellten AR-Applikationen im Besonderen Defizite bezüglich der Authentizität der Darstellungen auf. Dabei sind hauptsächlich inkorrekte Verdeckungen und unrealistische Schatten bei den virtuellen Objekten verantwortlich für den Verlust des Koexistenzeindrucks. In dieser Arbeit wird unter Berücksichtigung der Trackingprobleme und auf Basis von Analysen, die die wichtigsten Authentizitätskriterien bestimmen, ein Konzept zur authentischen Integration von virtuellen Objekten in AR-Anwendungen erarbeitet und dargelegt. Auf diesem Integrationsprozess basierend werden Konzepte für Werkzeuge mit grafischen Benutzungsschnittstellen abgeleitet, mit denen Autoren die Erstellung von AR-Anwendungen mit hoher Darstellungsauthentizität ermöglicht wird. Einerseits verfügen die mit diesen Werkzeugen erstellten AR-Anwendungen über eine verbesserte Registrierung der virtuellen Objekte. Andererseits stellen die Werkzeuge Lösungen bereit, damit die virtuellen Objekte der AR-Anwendungen korrekte Verdeckungen aufweisen und über Schatten und Schattierungseffekte verfügen, die mit der tatsächlichen Beleuchtungssituation der realen Umgebung übereinstimmen. Sämtliche dieser Autorenwerkzeuge basieren auf einem in dieser Arbeit dargelegten Prinzip, bei dem die authentische Integration mittels leicht verständlicher bzw. wenig komplexer Arbeitsschritte und auf Basis der Verwendung einer Bildsequenz der realen Zielumgebung stattfindet. Die Konzepte dieser Arbeit werden durch die Implementierung der Autorenwerkzeuge validiert. Dabei zeigt sich, dass die Konzepte technisch umsetzbar sind. Die Evaluierung basiert auf der Gegenüberstellung eines in dieser Arbeit entwickelten Anforderungskatalogs und verdeutlicht die Eignung des Integrationsprozesses und der davon abgeleiteten Konzepte der Autorenwerkzeuge. Die Autorenwerkzeuge werden in eine bestehende, frei verfügbare AR-Autorenumgebung integriert.
Das Projekt anan ist ein Werkzeug zur Fehlersuche in verteilten Hochleistungsrechnern. Die Neuheit des Beitrags besteht darin, dass die bekannten Methoden, die bereits erfolgreich zum Debuggen von Soft- und Hardware eingesetzt werden, auf Hochleistungs-Rechnen übertragen worden sind. Im Rahmen der vorliegenden Arbeit wurde ein Werkzeug namens anan implementiert, das bei der Fehlersuche hilft. Außerdem kann es als dynamischeres Monitoring eingesetzt werden. Beide Einsatzzwecke sind
getestet worden.
Das Werkzeug besteht aus zwei Teilen:
1. aus einem Teil namens anan, der interaktiv vom Nutzer bedient wird
2. und aus einem Teil namens anand, der automatisiert die verlangten Messwerte erhebt und nötigenfalls Befehle ausführt.
Der Teil anan führt Sensoren aus — kleine mustergesteuerte Algorithmen —, deren Ergebnisse per anan zusammengeführt werden. In erster Näherung lässt anan sich als Monitoring beschreiben, welches (1) schnell umkonfiguriert werden (2) komplexere Werte messen kann, die über Korrelationen einfacher Zeitreihen hinausgehen.
In the recent past, we are making huge progress in the field of Artificial Intelligence. Since the rise of neural networks, astonishing new frontiers are continuously being discovered. The development is so fast that overall no major technical limits are in sight. Hence, digitization has expanded from the base of academia and industry to such an extent that it is prevalent in the politics, mass media and even popular arts. The DFG-funded project Specialized Information Service for Biodiversity Research and the BMBF-funded project Linked Open Tafsir can be placed exactly in that overall development. Both projects aim to build an intelligent, up-to-date, modern research infrastructure on biodiversity and theological studies for scholars researching in these respective fields of historical science. Starting from digitized German and Arabic historical literature containing so far unavailable valuable knowledge on biodiversity and theological studies, at its core, our dissertation targets to incorporate state-of-the-art Machine Learning methods for analyzing natural language texts of low-resource languages and enabling foundational Natural Language Processing tasks on them, such as Sentence Boundary Detection, Named Entity Recognition, and Topic Modeling. This ultimately leads to paving the way for new scientific discoveries in the historical disciplines of natural science and humanities. By enriching the landscape of historical low-resource languages with valuable annotation data, our work becomes part of the greater movement of digitizing the society, thus allowing people to focus on things which really matter in science and industry.
The thesis deals with the analysis and modeling of point processes emerging from different experiments in neuroscience. In particular, the description and detection of different types of variability changes in point processes is of interest.
A non-stationary rate or variance of life times is a well-known problem in the description of point processes like neuronal spike trains and can affect the results of further analyses requiring stationarity. Moreover, non-stationary parameters might also contain important information themselves. The goal of the first part of the thesis is the (further) development of a technique to detect both rate and variance changes that may occur in multiple time scales separately or simultaneously. A two-step procedure building on the multiple filter test (Messer et al., 2014) is used that first tests the null hypothesis of rate homogeneity allowing for an inhomogeneous variance and that estimates change points in the rate if the null hypothesis is rejected. In the second step, the null hypothesis of variance homogeneity is tested and variance change points are estimated. Rate change points are used as input. The main idea is the comparison of estimated variances in adjacent windows of different sizes sliding over the process. To determine the rejection threshold functionals of the Brownian motion are identified as limit processes under the null of variance homogeneity. The non-parametric procedure is not restricted to the case of at most one change point. It is shown in simulation studies that the corresponding test keeps the asymptotic significance level for a wide range of parameters and that the test power is remarkable. The practical applicability of the procedure is underlined by the analysis of neuronal spike trains.
Point processes resulting from experiments on bistable perception are analyzed in the second part of the thesis. Visual illusions allowing for than more possible perception lead to unpredictable changes of perception. In the thesis data from (Schmack et al., 2015) are used. A rotating sphere with switching perceived rotation direction was presented to the participants of the study. The stimulus was presented continuously and intermittently, i.e., with short periods of „blank display“ between the presentation periods. There are remarkable differences in the response patterns between the two types of presentation. During continuous presentation the distribution of dominance times, i.e., the intervals of constant perception, is a right-skewed and unimodal distribution with a mean of about five seconds. In contrast, during intermittent presentation one observes very long, stable dominance times of more than one minute interchanging with very short, unstable dominance times of less than five seconds, i.e., an increase of variability.
The main goal of the second part is to develop a model for the response patterns to bistable perception that builds a bridge between empirical data analysis and mechanistic modeling. Thus, the model should be able to describe both the response patterns to continuous presentation and to intermittent presentation. Moreover, the model should be fittable to typically short experimental data, and the model should allow for neuronal correlates. Current approaches often use detailed assumptions and large parameter sets, which complicate parameter estimation.
First, a Hidden Markov Model is applied. Second, to allow for neuronal correlates, a Hierarchical Brownian Model (HBM) is introduced, where perception is modeled by the competition of two neuronal populations. The activity difference between these two populations is described by a Brownian motion with drift fluctuating between two borders, where each first hitting time causes a perceptual change. To model the response patterns to intermittent presentation a second layer with competing neuronal populations (coding a stable and an unstable state) is assumed. Again, the data are described very well, and the hypothesis that the relative time in the stable state is identical in a group of patients with schizophrenia and a control group is rejected. To sum up, the HBM intends to link empirical data analysis and mechanistic modeling and provides interesting new hypotheses on potential neuronal mechanisms of cognitive phenomena.
The ALICE High-Level-Trigger (HLT) is a large scale computing farm designed and constructed for the purpose of the realtime reconstruction of particle interactions (events) inside the ALICE detector. The reconstruction of such events is based on the raw data produced in collisions inside the ALICE at the Large Hadron Collider. The online reconstruction in the HLT allows the triggering on certain event topologies and a significant data reduction by applying compression algorithms. Moreover, it enables a real-time verification of the quality of the data.
To receive the raw data from the various sub-detectors of ALICE, the HLT is equipped with 226 custom built FPGA-based PCI-X cards, the H-RORCs. The H-RORC interfaces the detector readout electronics to the nodes of the HLT farm. In addition to the transfer of raw data, 108 H-RORCs host 216 Fast-Cluster-Finder (FCF) processors for the Time-Projection-Chamber (TPC). The TPC is the main tracking detector of ALICE and contributes with up to 16 GB/s to over 90% of the overall data volume. The FCF processor implements the first of two steps in the data reconstruction of the TPC. It calculates the space points and their properties from charge clouds (clusters) created by charged particles traversing the TPCs gas volume. Those space points are not only the base for the tracking algorithm, but also allow for a Huffman-based data compression, which reduces the data volume by a factor of 4 to 6.
The FCF processor is designed to cope with any incoming data rate up to the maximum bandwidth of the incoming optical link (160 MB/s) without creating back-pressure to the detectors readout electronics. A performance comparison with the software implementation of the algorithm shows a speedup factor of about 20 compared with one AMD Opteron 6172 Core @ 2.1 GHz, the CPU type used in the HLT during the LHC Run1 campaign. Comparison with an Intel E5-2690 Core @ 3.0 GHz, the CPU type used by the HLT for the LHC Run2 campaign, results in a speedup factor of 8.5. In total numbers, the 216 FCF processors provide the computing performance of 4255 AMD Opteron cores or 2203 Intel cores of the previously mentioned type. The performance of the reconstruction with respect to the physics analysis is equivalent or better than the official ALICE Offline clusterizer. Therefore, ALICE data taking was switched in 2011 to FCF cluster recording and compression only, discarding the raw data from the TPC. Due to the capability to compress the clusters, the recorded data volume could be increased by a factor of 4 to 6.
For the LHC Run3 campaign, starting in 2020, the FCF builds the foundation of the ALICE data taking and processing strategy. The raw data volume (before processing) of the upgraded TPC will exceed 3 TB/s. As a consequence, online processing of the raw data and compression of the results before it enters the online computing farms is an essential and crucial part of the computing model.
Within the scope of this thesis, the H-RORC card and the FCF processor were developed and built from scratch. It covers the conceptual design, the optimisation and implementation, as well as the verification. It is completed by performance benchmarks and experiences from real data taking.
One of the main things that we as humans do in our lifetime is the recognition and/or classification of all kind of visual objects. It is known that about fifty percentage of the neocortex is responsible for visual processing. This fact tells us that object recognition (OR) is a complex task in our and in the animal brain, but we do it in a fraction of a second.
The main question is: How does the brain exactly do it? Does the brain use some feature extraction algorithm for OR tasks? The hierarchical structure of the visual cortex and studies on a part of the visual cortex called V1 tell us that our brain uses feature extraction for OR tasks by Gabor filters. We also use our previous knowledge in object recognition to detect and recognize the objects which we never saw before. Also, as we grow up we learn new objects faster than before.
These facts imply that the visual cortex of human and other animals uses some common (universal) features at least in the first stages to distinguish between different objects. In this context, we might ask: Do universal features in images exist, such that by using them we are able to efficiently recognize any unknown object? Is it necessary to extract new special features for any new object? How about using existing features from other tasks for this? Is it possible to efficiently use extracted feature of a specific task for other tasks? Are there some general features in natural and non-natural images which can also be used for specific object recognition? For example, can we use extracted features of natural images also for handwritten digit classification?
In this context, our work proposes a new information-based approach and tries to give some answers to the questions above. As a result, in our case we found that we could indeed extract unique features which are valid in all three different kinds of tasks. They give classification results that are about as good as the results reported by the corresponding literature for the specialized systems, or even better ones.
Another problem of the OR task is the recognition of objects, independently of any perception changes. We as humans or also animals can recognize objects in spite of many deformations (e.g. changes in illumination, rotation in any direction or angles, distortion and scaling up or down) in a fraction of a second. When observing an object which we never saw, we can imagine the rotated or scaled up objectin our mind. Here, also the question arises: How does the brain solve this problem? To do this, does the brain learn some mapping algorithm (transformation), independent of the objects or their features?
There are many approaches to model the mapping task. One of the most versatile ones is the idea of dynamically changing mappings, the dynamic link mapping (DLM). Although the dynamic link mapping systems show interesting results, the DLM system has the problem of a high computational complexity. In addition, because it uses the least mean squared error as risk function, the performance for classification is also not optimal. For random values where outliers are present, this system may not work well because outliers influence the mean squared error classification much more than probability-based systems. Therefore, we would like to complete the DLM system by a modified approach.
In our contribution, we will introduce a new system which employs the information criteria (i.e. probabilities) to overcome the outlier problem of the DLM systems and has a smaller computational complexity. The new information based selforganised system can solve the problem of invariant object recognition, especially in the task of rotation in depth, and does not have the disadvantage of current DLM systems and has a smaller computational complexity.
Biologische Signalwege bilden komplexe Netzwerke aus, um die Zellantwort sensibel regulieren zu können. Systembiologische Ansätze werden eingesetzt, um biologische Systeme anhand von Computer-gestützten Modellen zu untersuchen. Ein mathematisches Modell erlaubt, neben der logischen Erfassung der Regulation des biologischen Systems, die systemweite Simulation des dynamischen Verhaltens und Analyse der Robustheit und Anfälligkeit.
Der TNFR1-vermittelte Signalweg reguliert essenzielle Zellvorgänge wie Entzündungsantworten,
Proliferation und Zelltod. TNFR1 wird von dem Zytokin TNF-α stimuliert und fördert daraufhin die Bildung verschiedener makromolekularer Komplexe, welche unterschiedliche Zellantworten einleiten, von der Aktivierung des Transkriptionsfaktors NF-κB, welcher die Expression von proliferationsfördernden Genen reguliert, bis zu zwei Formen des Zelltods, der Apoptose und der Nekroptose. Die Regulation der verschiedenen Zellantworten wird auch als molekularer Schalter bezeichnet. Die exakten molekularen Vorgänge, welche die Zellantwort modulieren, sind noch nicht vollständig entschlüsselt. Eine Fehlregulation des Signalwegs kann chronische Entzündungen hervorrufen oder die Entstehung von Tumoren fördern.
In dieser Thesis haben wir die neuesten Erkenntnisse der Forschung des TNFR1-Signalwegs anhand von umfangreichen Interaktionsdaten aus der Literatur erstmals in einem Petrinetz-Modell erfasst und analysiert. Das manuell kuratierte Modell umfasst die sequenziellen Prozesse der NF-κB-Aktivierung, Apoptose und Nekroptose und berücksichtigt den Einfluss posttranslationaler Modifikationen.
Weiterhin wurden Analysemethoden für Signalwegs-Modelle entwickelt, welche die spezifischen Anforderungen dieser biologischen Systeme berücksichtigen und eine biologisch motivierte Netzwerkanalyse ermöglichen. Die Manatee-Invarianten identifizieren Signalflüsse im Gleichgewichtszustand in Modellen, die Zyklen aufweisen, und werden als Linearkombination von Transitions-Invarianten gebildet. Diese Signalflüsse erfassen idealerweise einen Prozess von der Rezeptorstimulation zur Zellantwort in einem Modell eines Signalwegs. Die Bestimmung aller möglichen Signalflüsse in Modellen von Signalwegen ist eine notwendige Voraussetzung für weitere biologisch motivierte Analysen, wie die in silico-Knockout Analyse. Wir haben ebenfalls ein neues Konzept zur Untersuchung von in silico-Knockouts vorgestellt. Die Effekte der in silico-Knockouts auf einzelne Komplexe und Prozesse des Signalwegs werden in der in silico-Knockout-Matrix repräsentiert. Wir haben die Software-Anwendung isiKnock entwickelt, welche beide Konzepte kombiniert und eine systematische Knockout-Analyse von Petrinetz-Modellen unterstützt.
Das Petrinetz-Modell des TNFR1-Signalwegs wurde auf seine elementaren Eigenschaften geprüft und die etablierten Analysen wie Platz-Invarianten und Transitions-Invarianten durchgeführt. Hierbei konnten die Transitions-Invarianten nicht in allen Fällen komplette biologische Signalflüsse beschreiben. Wir haben ebenfalls die neu vorgestellten Methoden auf das Petrinetz-Modell angewandt. Anhand der Manatee-Invarianten konnten wir die zusammenhängenden Signalflüsse identifizieren und nach ihrem biologischen Ausgang klassifizieren sowie die Auswirkungen der Rückkopplungen untersuchen. Wir konnten zeigen, dass die survival-Antwort durch die Aktivierung von NF-κB am häufigsten auftritt, danach die Apoptose, gefolgt von der Nekroptose. Die alternativen Signalflüsse in Form der Manatee-Invarianten spiegeln die Robustheit des biologischen Systems wider. Wir führten eine ausgiebige in silico-Knockout-Analyse basierend auf den Manatee-Invarianten durch, um die Proteine des Signalwegs nach ihrem Einfluss einzustufen und zu gruppieren. Die Proteine des Komplex I wiesen hierbei den größten Einfluss auf, angeführt von der Rezeptorstimulation und RIP1. Wir betrachteten und diskutierten die Regulation des molekularen Schalters anhand der Knockout-Analyse von selektierten Proteinen und deren Auswirkung auf wichtige Komplexe im Modell. Wir identifizierten die Ubiquitinierung in Komplex I sowie die NF-κB-abhängige Genexpression als die wichtigen Kontrollpunkte des TNFR1-Signalwegs. In Komplex II ist die Regulation der Aktivierung der Caspase-Aktivität entscheidend.
Die umfangreiche Netzwerkanalyse basierend auf Manatee-Invarianten und systematischer in silico-Knockout-Analyse verifizierte das Petrinetz-Modell und erlaubte die Untersuchung der Robustheit und Anfälligkeit des Systems. Die neu entwickelten Methoden ermöglichen eine fundierte, biologisch relevante Untersuchung von in silico-Modellen von Signalwegen. Der systembiologische Ansatz unterstützt die Aufklärung der Regulation und Funktion des verflochtenen Netzwerks des TNFR1-Signalwegs.
Random ordinary differential equations (RODEs) are ordinary differential equations (ODEs) which have a stochastic process in their vector field functions. RODEs have been used in a wide range of applications such as biology, medicine, population dynamics and engineering and play an important role in the theory of random dynamical systems, however, they have been long overshadowed by stochastic differential equations.
Typically, the driving stochastic process has at most Hoelder continuous sample paths and the resulting vector field is, thus, at most Hoelder continuous in time, no matter how smooth the vector function is in its original variables, so the sample paths of the solution are certainly continuously differentiable, but their derivatives are at most Hoelder continuous in time. Consequently, although the classical numerical schemes for ODEs can be applied pathwise to RODEs, they do not achieve their traditional orders.
Recently, Gruene and Kloeden derived the explicit averaged Euler scheme by taking the average of the noise within the vector field. In addition, new forms of higher order Taylor-like schemes for RODEs are derived systematically by Jentzen and Kloeden.
However, it is still important to build higher order numerical schemes and computationally less expensive schemes as well as numerically stable schemes and this is the motivation of this thesis. The schemes by Gruene and Kloeden and Jentzen and Kloeden are very general, so RODEs with special structure, i.e., RODEs with Ito noise and RODEs with affine structure, are focused and numerical schemes which exploit these special structures are investigated.
The developed numerical schemes are applied to several mathematical models in biology and medicine. In order to see the performance of the numerical schemes, trajectories of solutions are illustrated. In addition, the error vs. step sizes as well as the computational costs are compared among newly developed schemes and the schemes in literature.
Driving can be dangerous. Humans become inattentive when performing a monotonous task like driving. Also the risk implied while multi-tasking, like using the cellular phone while driving, can break the concentration of the driver and increase the risk of accidents. Others factors like exhaustion, nervousness and excitement affect the performance of the driver and the response time. Consequently, car manufacturers have developed systems in the last decades which assist the driver under various circumstances. These systems are called driver assistance systems. Driver assistance systems are meant to support the task of driving, and the field of action varies from alerting the driver, with acoustical or optical warnings, to taking control of the car, such as keeping the vehicle in the traffic lane until the driver resumes control. For such a purpose, the vehicle is equipped with on-board sensors which allow the perception of the environment and/or the state of the vehicle. Cameras are sensors which extract useful information about the visual appearance of the environment. Additionally, a binocular system allows the extraction of 3D information. One of the main requirements for most camera-based driver assistance systems is the accurate knowledge of the motion of the vehicle. Some sources of information, like velocimeters and GPS, are of common use in vehicles today. Nevertheless, the resolution and accuracy usually achieved with these systems are not enough for many real-time applications. The computation of ego-motion from sequences of stereo images for the implementation of driving intelligent systems, like autonomous navigation or collision avoidance, constitutes the core of this thesis. This dissertation proposes a framework for the simultaneous computation of the 6 degrees of freedom of ego-motion (rotation and translation in 3D Euclidean space), the estimation of the scene structure and the detection and estimation of independently moving objects. The input is exclusively provided by a binocular system and the framework does not call for any data acquisition strategy, i.e. the stereo images are just processed as they are provided. Stereo allows one to establish correspondences between left and right images, estimating 3D points of the environment via triangulation. Likewise, feature tracking establishes correspondences between the images acquired at different time instances. When both are used together for a large number of points, the result is a set of clouds of 3D points with point-to-point correspondences between clouds. The apparent motion of the 3D points between consecutive frames is caused by a variety of reasons. The most dominant motion for most of the points in the clouds is caused by the ego-motion of the vehicle; as the vehicle moves and images are acquired, the relative position of the world points with respect to the vehicle changes. Motion is also caused by objects moving in the environment. They move independently of the vehicle motion, so the observed motion for these points is the sum of the ego-vehicle motion and the independent motion of the object. A third reason, and of paramount importance in vision applications, is caused by correspondence problems, i.e. the incorrect spatial or temporal assignment of the point-to-point correspondence. Furthermore, all the points in the clouds are actually noisy measurements of the real unknown 3D points of the environment. Solving ego-motion and scene structure from the clouds of points requires some previous analysis of the noise involved in the imaging process, and how it propagates as the data is processed. Therefore, this dissertation analyzes the noise properties of the 3D points obtained through stereo triangulation. This leads to the detection of a bias in the estimation of 3D position, which is corrected with a reformulation of the projection equation. Ego-motion is obtained by finding the rotation and translation between the two clouds of points. This problem is known as absolute orientation, and many solutions based on least squares have been proposed in the literature. This thesis reviews the available closed form solutions to the problem. The proposed framework is divided in three main blocks: 1) stereo and feature tracking computation, 2) ego-motion estimation and 3) estimation of 3D point position and 3D velocity. The first block solves the correspondence problem providing the clouds of points as output. No special implementation of this block is required in this thesis. The ego-motion block computes the motion of the cameras by finding the absolute orientation between the clouds of static points in the environment. Since the cloud of points might contain independently moving objects and outliers generated by false correspondences, the direct computation of the least squares might lead to an erroneous solution. The first contribution of this thesis is an effective rejection rule that detects outliers based on the distance between predicted and measured quantities, and reduces the effects of noisy measurement by assigning appropriate weights to the data. This method is called Smoothness Motion Constraint (SMC). The ego-motion of the camera between two frames is obtained finding the absolute orientation between consecutive clouds of weighted 3D points. The complete ego-motion since initialization is achieved concatenating the individual motion estimates. This leads to a super-linear propagation of the error, since noise is integrated. A second contribution of this dissertation is a predictor/corrector iterative method, which integrates the clouds of 3D points of multiple time instances for the computation of ego-motion. The presented method considerably reduces the accumulation of errors in the estimated ego-position of the camera. Another contribution of this dissertation is a method which recursively estimates the 3D world position of a point and its velocity; by fusing stereo, feature tracking and the estimated ego-motion in a Kalman Filter system. An improved estimation of point position is obtained this way, which is used in the subsequent system cycle resulting in an improved computation of ego-motion. The general contribution of this dissertation is a single framework for the real time computation of scene structure, independently moving objects and ego-motion for automotive applications.
Multi-view microscopy techniques are used to increase the resolution along the optical axis for 3D imaging. Without this, the resolution is insufficient to resolve subcellular events. In addition, parts of the images of opaque specimens are often highly degraded or masked. Both problems motivate scientists to record the same specimen from multiple directions. The images, then have to be digitally fused into a single high-quality image. Selective-plane illumination microscopy has proven to be a powerful imaging technique due to its unsurpassed acquisition speed and gentle optical sectioning. However, even in the case of multi view imaging techniques that illuminate and image the sample from multiple directions, light scattering inside tissues often severely impairs image contrast.
Here we show that for c-elegans embryos multi view registration can be achieved based on segmented nuclei. However, segmentation of nuclei in high density distribution like c-elegans embryo is challenging. We propose a method which uses 3D Mexican hat filter for preprocessing and 3D Gaussian curvature for the post-processing step to separate nuclei. We used this method successfully on 3 data sets of c-elegans embryos in 3 different views. The result of segmentation outperforms previous methods. Moreover, we provide a simple GUI for manual correction and adjusting the parameters for different data.
We then proposed a method that combines point and voxel registration for an accurate multi view reg- istration of c-elegans embryo, which does not need any special experimental preparation. We demonstrate the performance of our approach on data acquired from fixed embryos of c-elegans worms. This multi step approach is successfully evaluated by comparison to different methods and also by using synthetic data. The proposed method could overcome the typically low resolution along the optical axis and enable stitching to- gether the different parts of the embryo available through the different views. A tool for running the code and analyzing the results is developed.