Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images

Automatic segmentation and recognition of semantic classes in natural images is an important open problem in computer vision. In this work, we investigate three different approaches to recognition: without supervision, with supervision on level of images, and with supervision on the level of pixels. The thesis comprises three parts. The first part introduces a clustering algorithm that optimizes a novel information-theoretic objective function. We show that the proposed algorithm has clear advantages over standard algorithms from the literature on a wide array of datasets. Clustering algorithms are an important building block for higher-level computer vision applications, in particular for semantic segmentation. The second part of this work proposes an algorithm for automatic segmentation and recognition of object classes in natural images, that learns a segmentation model solely from annotation in the form of presence and absence of object classes in images. The third and main part of this work investigates one of the most popular approaches to the task of object class segmentation and semantic segmentation, based on conditional random fields and structured prediction. We investigate several learning algorithms, in particular in combination with approximate inference procedures. We show how structured models for image segmentation can be learned exactly in practical settings, even in the presence of many loops in the underlying neighborhood graphs. The introduced methods provide results advancing the state-of-the-art on two complex benchmark datasets for semantic segmentation, the MSRC-21 Dataset of RGB images and the NYU V2 Dataset or RGB-D images of indoor scenes. Finally, we introduce a software library that al- lows us to perform extensive empirical comparisons of state-of-the-art structured learning approaches. This allows us to characterize their practical properties in a range of applications, in particular for semantic segmentation and object class segmentation.Methoden zum Lernen von Strukturierter Vorhersage in Semantischer Segmentierung von Natürlichen Bildern Automatische Segmentierung und Erkennung von semantischen Klassen in natür- lichen Bildern ist ein wichtiges offenes Problem des maschinellen Sehens. In dieser Arbeit untersuchen wir drei möglichen Ansätze der Erkennung: ohne Überwachung, mit Überwachung auf Ebene von Bildern und mit Überwachung auf Ebene von Pixeln. Diese Arbeit setzt sich aus drei Teilen zusammen. Im ersten Teil der Arbeit schlagen wir einen Clustering-Algorithmus vor, der eine neuartige, informationstheoretische Zielfunktion optimiert. Wir zeigen, dass der vorgestellte Algorithmus üblichen Standardverfahren aus der Literatur gegenüber klare Vorteile auf vielen verschiedenen Datensätzen hat. Clustering ist ein wichtiger Baustein in vielen Applikationen des machinellen Sehens, insbesondere in der automatischen Segmentierung. Der zweite Teil dieser Arbeit stellt ein Verfahren zur automatischen Segmentierung und Erkennung von Objektklassen in natürlichen Bildern vor, das mit Hilfe von Supervision in Form von Klassen-Vorkommen auf Bildern in der Lage ist ein Segmentierungsmodell zu lernen. Der dritte Teil der Arbeit untersucht einen der am weitesten verbreiteten Ansätze zur semantischen Segmentierung und Objektklassensegmentierung, Conditional Random Fields, verbunden mit Verfahren der strukturierten Vorhersage. Wir untersuchen verschiedene Lernalgorithmen des strukturierten Lernens, insbesondere im Zusammenhang mit approximativer Vorhersage. Wir zeigen, dass es möglich ist trotz des Vorhandenseins von Kreisen in den betrachteten Nachbarschaftsgraphen exakte strukturierte Modelle zur Bildsegmentierung zu lernen. Mit den vorgestellten Methoden bringen wir den Stand der Kunst auf zwei komplexen Datensätzen zur semantischen Segmentierung voran, dem MSRC-21 Datensatz von RGB-Bildern und dem NYU V2 Datensatz von RGB-D Bildern von Innenraum-Szenen. Wir stellen außerdem eine Software-Bibliothek vor, die es erlaubt einen weitreichenden Vergleich der besten Lernverfahren für strukturiertes Lernen durchzuführen. Unsere Studie erlaubt uns eine Charakterisierung der betrachteten Algorithmen in einer Reihe von Anwendungen, insbesondere der semantischen Segmentierung und Objektklassensegmentierung

Determining the Enantioselectivity of Chiral Catalysts by Mass Spectrometric Screening of Their Racemic Forms

The enantioselectivity of a chiral catalyst can be determined from its racemic form by mass spectrometric screening of a nonequal mixture of two mass-labeled quasienantiomeric substrates. The presented method opens up new possibilities for evaluating catalyst structures that are not readily available in enantiomerically pure form

Comorbidities Associated with Large Abdominal Aortic Aneurysms

BACKGROUND: Abdominal aortic aneurysm has become increasingly important owing to demographic changes. Some other diseases, for example, cholecystolithiasis, chronic obstructive pulmonary disease, and hernias, seem to co-occur with abdominal aortic aneurysm. The aim of this retrospective analysis was to identify new comorbidities associated with abdominal aortic aneurysm. METHODS: We compared 100 patients with abdominal aortic aneurysms and 100 control patients. Their preoperative computed tomographic scans were examined by two investigators independently, for the presence of hernias, diverticulosis, and cholecystolithiasis. Medical records were also reviewed. Statistical analysis was performed using univariate analysis and multiple logistic regression analysis. RESULTS: The aneurysm group had a higher frequency of diverticulosis (p = 0.008). There was no significant difference in the occurrence of hernia (p = 0.073) or cholecystolithiasis (p = 1.00). Aneurysm patients had a significantly higher American Society of Anesthesiology score (2.84 vs. 2.63; p = 0.015) and were more likely to have coronary artery disease (p < 0.001), congestive heart failure (p < 0.001), or chronic obstructive pulmonary disease (p < 0.001). Aneurysm patients were more likely to be former (p = 0.034) or current (p = 0.006) smokers and had a significantly higher number of pack years (p < 0.001). Aneurysm patients also had a significantly poorer lung function. In multivariate analysis, the following factors were associated with aneurysms: chronic obstructive pulmonary disease (odds ratio, OR = 12.24; p = 0.002), current smoking (OR = 4.14; p = 0.002), and coronary artery disease (OR = 2.60; p = 0.020). CONCLUSIONS: Our comprehensive analysis identified several comorbidities associated with abdominal aortic aneurysms. These results could help to recognize aneurysms earlier by targeting individuals with these comorbidities for screening

Creating a connection between asphalt wearing surface and timber bridge decks

Timber bridges are increasingly popular again in infrastructure networks nowadays. Protection of timber structural elements from direct exposure to rain and sun is essential to prevent biological degradation of the wood material when using softwoods. In case of a timber deck, the asphalt wearing surface must be water-tight. Creating connected layups has advantages over layups where the asphalt is not connected as traffic loads are transmitted directly to the bridge deck: asphalt surfaces can be loaded with heavier traffic, slopes of bridges can be higher, breaking and acceleration loads are directly transmitted to the bridge deck, and fatigue life of the asphalt is improved. The Swiss standard VSS 40 451 standardized an unconnected mastic asphalt surfacing on timber bridges. Research project VSS 2016/326 was performed to investigate road layups where asphalt wearing surfaces were connected to the bridge deck. This work presents only a part of the results, focused on the bonding issues. Results show that such a layup proves to deliver satisfactory performances, and that shear capacity is comparable to that of concrete and steel decks. Good surface qual-ity of the timber deck is important before application of the bonding agent to avoid short term blistering of the asphalt

Microwave photon-mediated interactions between semiconductor qubits

The realization of a coherent interface between distant charge or spin qubits in semiconductor quantum dots is an open challenge for quantum information processing. Here we demonstrate both resonant and non-resonant photon-mediated coherent interactions between double quantum dot charge qubits separated by several tens of micrometers. We present clear spectroscopic evidence of the collective enhancement of the resonant coupling of two qubits. With both qubits detuned from the resonator we observe exchange coupling between the qubits mediated by virtual photons. In both instances pronounced bright and dark states governed by the symmetry of the qubit-field interaction are found. Our observations are in excellent quantitative agreement with master-equation simulations. The extracted two-qubit coupling strengths significantly exceed the linewidths of the combined resonator-qubit system. This indicates that this approach is viable for creating photon-mediated two-qubit gates in quantum dot based systems.Comment: 14 pages, 10 figures and 6 table

Multi-Lens Array Full-Field X-ray Microscopy

X-ray full-field microscopy at laboratory sources for photon energies above 10 keV suffers from either long exposure times or low resolution. The photon flux is mainly limited by the objectives used, having a limited numerical aperture NA. We show that this can be overcome by making use of the cone-beam illumination of laboratory sources by imaging the same field of view (FoV) several times under slightly different angles using an array of X-ray lenses. Using this technique, the exposure time can be reduced drastically without any loss in terms of resolution. A proof-of-principle is given using an existing laboratory metal-jet source at the 9.25 keV Ga Kα-line and compared to a ray-tracing simulation of the setup