Die Begutachtung, der Handel und das Sammeln von Kunstgegenständen findet nicht ausschließlich analog statt. Die Firma 4ARTechnologies entwickelt Softwarelösungen für das digitale Kollektionsmanagement physischer und digitaler Kunst. Mittels Applikationen auf mobilen Endgeräten können Nutzer ihre Gemälde registrieren, authentifizieren und periodisch präzise Zustandsberichte erstellen. Die Erstellung von Zustandsberichten führt jedoch aufgrund von menschlichen Limitierungen zu Problemen in der Handhabung der Applikation und soll mithilfe eines mobilen Roboters automatisiert werden. Das Ziel dieser Arbeit ist die Entwicklung einer Navigation für einen mobilen Roboter. Diese soll folgendes Problem lösen: Lokalisierung eines Gemäldes, kollisionsfreie Annäherung und horizontal-mittige Positionierung davor. Zielplattform dieser Software ist das mobile Betriebssystem iOS. Für die Lösung wurden Verfahren der Navigation mobiler Roboter und der computergestützten Erkennung von Bildern untersucht. Die Navigationssoftware nutzt zur Zielfindung das Feature-Matching aus der OpenCV-Bibliothek. Für die Schätzung der eigenen Position werden relative Lokalisierungverfahren wie Posenverfolgung und Odometrie eingesetzt. Die Abbildung der Umgebung sowie der Bewegungsverlauf des Roboters werden auf einer topologischen Karte dargestellt. Mittels implementiertem BUG3-Algorithmus werden Hindernisse umfahren.:1. Einleitung 1.1. Problembeschreibung und thematische Abgrenzung 1.2. Aufbau Roboter 1.3. Randbedingungen und Anforderungen 2. Theoretische Grundlagen 2.1. Robotik 2.1.1. Mobile Robotik 2.2. Navigation 2.2.1. Lokalisierung 2.2.2. Kartierung 2.2.3. SLAM 2.2.4. Pfadfindung 2.2.5. Augmented Reality 2.3. Computer Vision 2.3.1. OpenCV 2.3.2. Vorlagen Erkennung 2.3.3. Template-basiertes Matching 2.3.4. Feature-basiertes Matching 3. Praktische Umsetzung 3.1. Programmablauf der Navigation 3.1.1. Verbindung mit dem Roboter 3.1.2. Initiale Exploration 3.1.3. Lokalisation und Annäherung 3.1.4. Kollisionsvermeidung 3.1.5. Zielanfahrt und Positionierung 4. Tests 4.1. Störfaktoren 5. Fazit und Ausblick 5.1. Fazit 5.2. Ausblick / The appraisal, trading and collecting of art objects does not only take place analogously. The company 4ARTechnologies develops software solutions for the digital collection management of physical and digital art. Using applications on mobile devices, users can register and authenticate their paintings and periodically create precise condition reports. The creation of condition reports leads to problems in handling the application due to human limitations and should be automated with the help of a mobile robot. The goal of this work is the development of a navigation system for a mobile robot. This should solve the following problem: Localization of a painting and the collision-free arrival and horizontal-center position in front of it. The target platform of this software is the mobile operating system iOS. Several methods, including the navigation of mobile robots and the computer-aided recognition of images were examined for the solution. The navigation software uses feature matching from the Open-CV library to find the destination. Relative localization methods such as pose tracking and odometry are used to estimate the robots own position. The environment and the movement of the robot are shown in a topological map. Obstacles are bypassed using the implemented BUG3 algorithm.:1. Einleitung 1.1. Problembeschreibung und thematische Abgrenzung 1.2. Aufbau Roboter 1.3. Randbedingungen und Anforderungen 2. Theoretische Grundlagen 2.1. Robotik 2.1.1. Mobile Robotik 2.2. Navigation 2.2.1. Lokalisierung 2.2.2. Kartierung 2.2.3. SLAM 2.2.4. Pfadfindung 2.2.5. Augmented Reality 2.3. Computer Vision 2.3.1. OpenCV 2.3.2. Vorlagen Erkennung 2.3.3. Template-basiertes Matching 2.3.4. Feature-basiertes Matching 3. Praktische Umsetzung 3.1. Programmablauf der Navigation 3.1.1. Verbindung mit dem Roboter 3.1.2. Initiale Exploration 3.1.3. Lokalisation und Annäherung 3.1.4. Kollisionsvermeidung 3.1.5. Zielanfahrt und Positionierung 4. Tests 4.1. Störfaktoren 5. Fazit und Ausblick 5.1. Fazit 5.2. Ausblick

Recent advancements in empirical and certified robustness have shown promising results in developing reliable and deployable Deep Neural Networks (DNNs). However, most evaluations of DNN robustness have focused on testing models on images from the same distribution they were trained on. In real-world scenarios, DNNs may encounter dynamic environments with significant distribution shifts. This thesis aims to investigate the interplay between empirical and certified adversarial robustness and domain generalization. We take the first step by training robust models on multiple domains and evaluating their accuracy and robustness on an unseen domain. Our findings reveal that: (1) both empirical and certified robustness exhibit generalization to unseen domains, and (2) the level of generalizability does not correlate strongly with the visual similarity of inputs, as measured by the Fréchet Inception Distance (FID) between source and target domains. Furthermore, we extend our study to a real-world medical application, where we demonstrate that adversarial augmentation significantly enhances robustness generalization while minimally affecting accuracy on clean data. This research sheds light on the importance of evaluating DNNs under real-world distribution shifts and highlights the potential of adversarial augmentation in improving robustness in practical applications.

Yes / The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing. © 2011, IGI Global.

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Matting, which aims to separate the foreground object from the background of an image, is an important problem in computer vision. Most existing methods rely on auxiliary information such as trimaps or scibbles to alleviate the difficulty arising from the underdetermined nature of the matting problem. However, such methods tend to be sensitive to the quality of auxiliary information, and are unsuitable for real-time deployment. In this paper, we propose a novel Attention-based Multi-scale Matting Network (AMMNet), which can estimate the alpha matte from a given RGB image without resorting to any auxiliary information. The proposed AMMNet consists of three (sub-)networks: 1) a multi-scale neural network designed to provide the semantic information of the foreground object, 2) a Unet-like network for attention mask generation, and 3) a Convolutional Neural Network (CNN) customized to integrate high- and low-level features extracted by the first two (sub-)networks. The AMMNet is generic in nature and can be trained end-to-end in a straightforward manner. The experimental results indicate that the performance of AMMNet is competitive against the state-of-the-art matting methods, which either require additional side information or are tailored to images with a specific type of content (e.g., portrait). / Thesis / Master of Applied Science (MASc)

One of the major problems in computer vision involves dealing with uncertain information. Occlusion, dissimilar views, insufficient illumination, insufficient resolution, and degradation give rise to imprecise data. At the same time, incomplete or local knowledge of the scene gives rise to imprecise interpretation rules. Uncertainty arises at different processing levels of computer vision either because of the imprecise data or because of the imprecise interpretation rules. It is natural to build computer vision systems that incorporate uncertainty reasoning. The Dempster-Shafer (D-S) theory of evidence is appealing for coping with uncertainty hierarchically. However, very little work has been done to apply D-S theory to practical vision systems because some important problems are yet to be resolved. / Ph. D.