Graph-based Object Understanding

Computer Vision algorithms become increasingly prevalent in our everyday lives. Especially recognition systems are often employed to automatize certain tasks (i.e. quality control). In State-of-the-Art approaches global shape char acteristics are leveraged, discarding nuanced shape varieties in the individual parts of the object. Thus, these systems fall short on both learning and utilizing the inherent underlying part structures of objects. By recognizing common substructures between known and queried objects, part-based systems may identify objects more robustly in lieu of occlusion or redundant parts. As we observe these traits, there are theories that such part-based approaches are indeed present in humans. Leveraging abstracted representations of decomposed objects may additionally offer better generalization on less training data. Enabling computer systems to reason about objects on the basis of their parts is the focus of this dissertation. Any part-based method first requires a segmentation approach to assign object regions to individual parts. Therefore, a 2D multi-view segmentation approach for 3D mesh segmentation is extended. The approach uses the normal and depth information of the objects to reliably extract part boundary contours. This method significantly reduces training time of the segmentation model compared to other segmentation approaches while still providing good segmentation results on the test data. To explore the benefits of part-based systems, a symbolic object classification dataset is created that inherently adheres to underlying rules made of spatial relations between part entities. This abstract data is also transformed into 3D point clouds. This enables us to benchmark conventional 3D point cloud classification models against the newly developed model that utilizes ground truth symbol segmentations for the classification task. With the new model, improved classification performance can be observed. This offers empirical evidence that part segmentation may boost classification accuracy if the data obey part-based rules. Additionally, prediction results of the model on segmented 3D data are compared against a modified variant of the model that directly uses the underlying symbols. The perception gap, representing issues with extracting the symbols from the segmented point clouds, is quantified. Furthermore, a framework for 3D object classification on real world objects is developed. The designed pipeline automatically segments an object into its parts, creates the according part graph and predicts the object class based on the similarity to graphs in the training dataset. The advantage of subgraph similarity is utilized in a second experiment, where out-of-distribution samples ofobjects are created, which contain redundant parts. Whereas traditional classification methods working on the global shape may misinterpret extracted feature vectors, the model creates robust predictions. Lastly, the task of object repairment is considered, in which a single part of the given object is compromised by a certain manipulation. As human-made objects follow an underlying part structure, a system to exploit this part structure in order to mend the object is developed. Given the global 3D point cloud of a compromised object, the object is automatically segmented, the shape features are extracted from the individual part clouds and are fed into a Graph Neural Network that predicts a manipulation action for each part. In conclusion, the opportunities of part-graph based methods for object understanding to improve 3D classification and regression tasks are explored. These approaches may enhance robotic computer vision pipelines in the future.2021-06-2

Rekonstruktion und skalierbare Detektion und Verfolgung von 3D Objekten

The task of detecting objects in images is essential for autonomous systems to categorize, comprehend and eventually navigate or manipulate its environment. Since many applications demand not only detection of objects but also the estimation of their exact poses, 3D CAD models can prove helpful since they provide means for feature extraction and hypothesis refinement. This work, therefore, explores two paths: firstly, we will look into methods to create richly-textured and geometrically accurate models of real-life objects. Using these reconstructions as a basis, we will investigate on how to improve in the domain of 3D object detection and pose estimation, focusing especially on scalability, i.e. the problem of dealing with multiple objects simultaneously.Objekterkennung in Bildern ist für ein autonomes System von entscheidender Bedeutung, um seine Umgebung zu kategorisieren, zu erfassen und schließlich zu navigieren oder zu manipulieren. Da viele Anwendungen nicht nur die Erkennung von Objekten, sondern auch die Schätzung ihrer exakten Positionen erfordern, können sich 3D-CAD-Modelle als hilfreich erweisen, da sie Mittel zur Merkmalsextraktion und Verfeinerung von Hypothesen bereitstellen. In dieser Arbeit werden daher zwei Wege untersucht: Erstens werden wir Methoden untersuchen, um strukturreiche und geometrisch genaue Modelle realer Objekte zu erstellen. Auf der Grundlage dieser Konstruktionen werden wir untersuchen, wie sich der Bereich der 3D-Objekterkennung und der Posenschätzung verbessern lässt, wobei insbesondere die Skalierbarkeit im Vordergrund steht, d.h. das Problem der gleichzeitigen Bearbeitung mehrerer Objekte

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

EFFECTS OF AUGMENTED REALITY BASED OBJECT ILLUMINATION ON HUMAN PERFORMANCE

Extravehicular Activities (EVAs) in space are generally considered to be high-risk, costly activities, due to the nature of the working environment and the limitations imposed on astronaut mobility and dexterity. Procedures are scheduled out and rehearsed far in advance, with time being considered a precious commodity during missions. Providing artificial task guidance to astronauts could potentially improve their efficiency, enabling for shorter duration EVAs and/or a larger quantity of tasks completed. This research quantitatively measured the effects of virtually illuminating or “cueing” objects of interest on a user’s ability to complete a predefined task, through the use of augmented reality (AR) “active display” symbology. This was achieved through the implementation of a Microsoft HoloLens™ head mounted display. It was demonstrated that, after controlling for a variety of factors, virtual illumination techniques improved task completion speed by approximately 100% and reduced perceived mental workload, with no adverse effects on accuracy

Mesoscale constitutive behavior of ferroelectrics

The main goal of this study is the in-situ investigation of the ferroelectric domain structure inside polycrystalline BaTiO3 under thermo-electro-mechanical loading conditions. The outcome is two-fold: (i) the characterization techniques were improved to study the polycrystalline ferroelectrics in the mesoscale; and (ii) the texture, lattice strain and volume fraction of domains were tracked under applied electric field and mechanical stress. Two novel synchrotron-based characterization techniques, three-dimensional X-ray diffraction (3-D XRD) and Scanning X-ray Microdiffraction (ySXRD) were used in this study. The methodology and standards in both techniques differ from each other and the present study provides a framework to bridge these techniques. Although these methods have been developed earlier, their application and adaptation to ferroelectrics required some care. For instance, diffraction spots often overlapped and made it difficult to identify individual domains and/or grains. In order to eliminate the spot overlap, the polycrystalline BaTiO3 sample was heated above the Curie temperature where the (tetragonal) domains disappear and attain the orientation of the grain. Next, the sample was cooled slowly to the room temperature and the evolution of the ferroelectric domains was studied at temperature and under electric field. The orientation relationships, volume fractions and lattice strain evolution of 8 domain systems were studied. Whereas the orientation of the domains remained unchanged under electric field, the fraction of the energetically favorable domain variants increased. Due to local constraints, complete switching from one domain variant to another was not observed. The misorientation angles between domain variants slightly deviated from the theoretical value (=89.4y) by 0.2-0.3y. The deviation angle can be explained with the phase-matching angle developed during the cubic-tetragonal phase transformation to maintain strain compatibility of neighboring domains. The multiscale strain evolution of ferroelectric domains in a polycrystal was investigated quantitatively for the first time. Under electric field, lattice strains of up to 0.1% were measured along the applied field direction. The present study offers a framework to characterize the polycrystalline materials with complex twin structures. By using the methodology described in this study, 3D-XRD and ySXRD techniques can be employed to study texture and lattice strain evolution in polycrystalline materials in the mesoscale

Symmetry Detection in Geometric Models

PUNTIS (LO1506), SGS-2019-016Symmetry occurs very commonly in real world objects as well as in artificially created geometric models. The knowledge about symmetry of a given object can be very useful in many applications in computer graphics and geometry processing, such as compression, object alignment, symmetric editing or completion of partial objects. In order to use the symmetry of any object in any given application, it first needs to be found. In this work, we provide some background about symmetry in general and about different types of symmetry, mainly in 3D objects. Then we focus on the task of automatic symmetry detection in 3D objects and we also describe the link between symmetry detection and the problem of registration. Most importantly, we present our own contribution in these fields. First, we show a new method of evaluating consensus in RANSAC surface registration together with a thorough analysis of various distance metrics for rigid transformations that can be used in this new approach. Afterwards, we provide an analysis of different representations of the space of planes in context of symmetry plane detection. At last, we propose a new, robust, fast and flexible method for symmetry plane detection based on a novel differentiable symmetry measure