Crosstalk in stereoscopic displays

Crosstalk is an important image quality attribute of stereoscopic 3D displays. The research presented in this thesis examines the presence, mechanisms, simulation, and reduction of crosstalk for a selection of stereoscopic display technologies. High levels of crosstalk degrade the perceived quality of stereoscopic displays hence it is important to minimise crosstalk. This thesis provides new insights which are critical to a detailed understanding of crosstalk and consequently to the development of effective crosstalk reduction techniques

Volume-resolved gas velocity and spray measurements in engine applications

The ability to visualize in-cylinder phenomena in a three-dimensional (3D) manner is critical to further understand the complex physical and chemical processes within internal-combustion (IC) engines. Recently, plenoptic imaging techniques have been introduced to engine studies because they enable 3D measurements using a promising and simple single-camera setup. The fundamental concept is to record both the origin and direction of each light ray into a single light-field image by inserting a micro-lens array in front of the photosensor. Therefore, a single image contains enough information to reconstruct the 3D volume. In this study, we present the implementation of a plenoptic technique that allows 3D measurements of fuel-spray structure, as well as three-dimensional, three-component (3D3C) particle tracking velocimetry (PTV) of engine in-cylinder air flow. Flow-spray interactions and the impact on the 3D geometry of fuel sprays were investigated with single-shot plenoptic imaging. Volume-illuminated fuel sprays from a multi-hole injector were examined in an optically accessible four-valve gasoline direct-injection engine. The impact of air flows during the intake and compression strokes on the shape of the fuel plumes could readily be observed for individual sprays without averaging. The air flow was measured in a free jet flow and a steady-state engine flow bench employing a 3D3C PTV algorithm that analyzed volume-resolved images taken with a plenoptic camera. Silicone seed oil droplets were added to the air flows and were illuminated by the volume-expanded beam of a double-pulsed laser. Mie scattering from the droplets was recorded by the plenoptic camera, which was operated in double-frame mode. Results from the 3D3C PTV measurements were compared to two-dimensional (2D) planar particle-image velocimetry (PIV) and demonstrate the capability of the 3D velocimetry approach, presently delivering averaged flow fields.This material is based upon work supported by the National Science Foundation under Grant No. CBET 1402707.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139882/1/Chen_Sick_AVL2016.pd

Environmental Odour

Environmental odour is perceived as a major nuisance by rural as well as urban populations. The sources of odourous substances are manifold. In urban areas, these include restaurants, small manufacturing trades, and other sources, which might cause complaints. In the suburbs, wastewater treatment plants, landfill sites, and other infrastructures are the expected major odour sources. These problems are often caused be the accelerated growth of cities. In rural sites, livestock farming and the spreading of manure on the fields is blamed for severe odour annoyance. In fact, environmental odours are considered to be a common cause of public complaints by residents to local authorities, regional, or national environmental agencies. This Special Issue of Atmosphere will address the entire chain, from the quantification of odour sources, abatement methods, the dilution in the atmosphere, and the assessment of odour exposure for the assessment of annoyance. In particular, this Special Issue aims to encourage contributions dealing with field trials and dispersion modeling to assess the degree of annoyance and the quantitative success of abatement measures

Evaluating Evolving Leukocyte Populations In Peripheral Blood Circulation Post-Concussion In A Human Longitudinal Analysis Of Female Athletes

Concussions are generating increasing concern due to potential long-term neurological consequences. Currently there is no universally recognized diagnostic approach for concussion. I hypothesize that a signature temporal response of biomarkers of inflammation in systemic circulation will provide an objective diagnosis of concussion and could also be used to track patient recovery. The Western University women’s rugby team underwent blood draws at pre-season and post-season as a baseline evaluation, and players determined to have sustained a concussion underwent repeat blood analysis post-concussion. Blood samples were analyzed by flow cytometry to profile immune cell populations alongside accepted concussion assessments, and complete blood count. Immune profiles demonstrated significant changes in total leukocytes and subsets post-concussion compared to baseline. It was demonstrated that we could successfully and feasibly recruit and perform a discovery investigation into potential blood biomarkers of concussion longitudinally. My study provides new insights for future blood biomarker research of concussive injury

Convolutional Neural Networks for Image Steganalysis in the Spatial Domain

Esta tesis doctoral muestra los resultados obtenidos al aplicar Redes Neuronales Convolucionales (CNNs) para el estegoanálisis de imágenes digitales en el dominio espacial. La esteganografía consiste en ocultar mensajes dentro de un objeto conocido como portador para establecer un canal de comunicación encubierto para que el acto de comunicación pase desapercibido para los observadores que tienen acceso a ese canal. Steganalysis se dedica a detectar mensajes ocultos mediante esteganografía; estos mensajes pueden estar implícitos en diferentes tipos de medios, como imágenes digitales, archivos de video, archivos de audio o texto sin formato. Desde 2014, los investigadores se han interesado especialmente en aplicar técnicas de Deep Learning (DL) para lograr resultados que superen los métodos tradicionales de Machine Learning (ML).Is doctoral thesis shows the results obtained by applying Convolutional Neural Networks (CNNs) for the steganalysis of digital images in the spatial domain. Steganography consists of hiding messages inside an object known as a carrier to establish a covert communication channel so that the act of communication goes unnoticed by observers who have access to that channel. Steganalysis is dedicated to detecting hidden messages using steganography; these messages can be implicit in di.erent types of media, such as digital images, video €les, audio €les, or plain text. Since 2014 researchers have taken a particular interest in applying Deep Learning (DL) techniques to achieving results that surpass traditional Machine Learning (ML) methods

From pixels to people : recovering location, shape and pose of humans in images

Humans are at the centre of a significant amount of research in computer vision. Endowing machines with the ability to perceive people from visual data is an immense scientific challenge with a high degree of direct practical relevance. Success in automatic perception can be measured at different levels of abstraction, and this will depend on which intelligent behaviour we are trying to replicate: the ability to localise persons in an image or in the environment, understanding how persons are moving at the skeleton and at the surface level, interpreting their interactions with the environment including with other people, and perhaps even anticipating future actions. In this thesis we tackle different sub-problems of the broad research area referred to as "looking at people", aiming to perceive humans in images at different levels of granularity. We start with bounding box-level pedestrian detection: We present a retrospective analysis of methods published in the decade preceding our work, identifying various strands of research that have advanced the state of the art. With quantitative exper- iments, we demonstrate the critical role of developing better feature representations and having the right training distribution. We then contribute two methods based on the insights derived from our analysis: one that combines the strongest aspects of past detectors and another that focuses purely on learning representations. The latter method outperforms more complicated approaches, especially those based on hand- crafted features. We conclude our work on pedestrian detection with a forward-looking analysis that maps out potential avenues for future research. We then turn to pixel-level methods: Perceiving humans requires us to both separate them precisely from the background and identify their surroundings. To this end, we introduce Cityscapes, a large-scale dataset for street scene understanding. This has since established itself as a go-to benchmark for segmentation and detection. We additionally develop methods that relax the requirement for expensive pixel-level annotations, focusing on the task of boundary detection, i.e. identifying the outlines of relevant objects and surfaces. Next, we make the jump from pixels to 3D surfaces, from localising and labelling to fine-grained spatial understanding. We contribute a method for recovering 3D human shape and pose, which marries the advantages of learning-based and model- based approaches. We conclude the thesis with a detailed discussion of benchmarking practices in computer vision. Among other things, we argue that the design of future datasets should be driven by the general goal of combinatorial robustness besides task-specific considerations.Der Mensch steht im Zentrum vieler Forschungsanstrengungen im Bereich des maschinellen Sehens. Es ist eine immense wissenschaftliche Herausforderung mit hohem unmittelbarem Praxisbezug, Maschinen mit der Fähigkeit auszustatten, Menschen auf der Grundlage von visuellen Daten wahrzunehmen. Die automatische Wahrnehmung kann auf verschiedenen Abstraktionsebenen erfolgen. Dies hängt davon ab, welches intelligente Verhalten wir nachbilden wollen: die Fähigkeit, Personen auf der Bildfläche oder im 3D-Raum zu lokalisieren, die Bewegungen von Körperteilen und Körperoberflächen zu erfassen, Interaktionen einer Person mit ihrer Umgebung einschließlich mit anderen Menschen zu deuten, und vielleicht sogar zukünftige Handlungen zu antizipieren. In dieser Arbeit beschäftigen wir uns mit verschiedenen Teilproblemen die dem breiten Forschungsgebiet "Betrachten von Menschen" gehören. Beginnend mit der Fußgängererkennung präsentieren wir eine Analyse von Methoden, die im Jahrzehnt vor unserem Ausgangspunkt veröffentlicht wurden, und identifizieren dabei verschiedene Forschungsstränge, die den Stand der Technik vorangetrieben haben. Unsere quantitativen Experimente zeigen die entscheidende Rolle sowohl der Entwicklung besserer Bildmerkmale als auch der Trainingsdatenverteilung. Anschließend tragen wir zwei Methoden bei, die auf den Erkenntnissen unserer Analyse basieren: eine Methode, die die stärksten Aspekte vergangener Detektoren kombiniert, eine andere, die sich im Wesentlichen auf das Lernen von Bildmerkmalen konzentriert. Letztere übertrifft kompliziertere Methoden, insbesondere solche, die auf handgefertigten Bildmerkmalen basieren. Wir schließen unsere Arbeit zur Fußgängererkennung mit einer vorausschauenden Analyse ab, die mögliche Wege für die zukünftige Forschung aufzeigt. Anschließend wenden wir uns Methoden zu, die Entscheidungen auf Pixelebene betreffen. Um Menschen wahrzunehmen, müssen wir diese sowohl praezise vom Hintergrund trennen als auch ihre Umgebung verstehen. Zu diesem Zweck führen wir Cityscapes ein, einen umfangreichen Datensatz zum Verständnis von Straßenszenen. Dieser hat sich seitdem als Standardbenchmark für Segmentierung und Erkennung etabliert. Darüber hinaus entwickeln wir Methoden, die die Notwendigkeit teurer Annotationen auf Pixelebene reduzieren. Wir konzentrieren uns hierbei auf die Aufgabe der Umgrenzungserkennung, d. h. das Erkennen der Umrisse relevanter Objekte und Oberflächen. Als nächstes machen wir den Sprung von Pixeln zu 3D-Oberflächen, vom Lokalisieren und Beschriften zum präzisen räumlichen Verständnis. Wir tragen eine Methode zur Schätzung der 3D-Körperoberfläche sowie der 3D-Körperpose bei, die die Vorteile von lernbasierten und modellbasierten Ansätzen vereint. Wir schließen die Arbeit mit einer ausführlichen Diskussion von Evaluationspraktiken im maschinellen Sehen ab. Unter anderem argumentieren wir, dass der Entwurf zukünftiger Datensätze neben aufgabenspezifischen Überlegungen vom allgemeinen Ziel der kombinatorischen Robustheit bestimmt werden sollte

Live Cell Interferometry: A Novel Quantitative Phase Imaging Technique for Rapid Characterizations of Tumor Heterogeneity, Drug Resistance, Cell Fates and Biophysical Properties

Cell mass is an important biophysical property that provides a crucial link between external physical measurements and internal cellular processes such as cell cycle progression, division, differentiation and cell death. Advancements in quantitative phase imaging (QPI) techniques have enabled many in-depth studies of cell mass in the context of basic and translational research. This thesis describes the development and implementation of live cell interferometry (LCI) as a novel QPI technique that is high speed, high throughput, precise and label-free. It was validated as a powerful tool in dissecting tumor heterogeneity and drug resistance in patient derived melanoma cell lines. LCI analysis of cell fate in response to mitotic inhibitors provided valuable insights in cancer drug development and dose selection. Furthermore, it was utilized to characterize many other biophysical responses in fundamental research such as cardiomyocyte hypertrophy in cardiac wound healing responses. These studies showcased the unique capabilities and advantages of LCI in applications from bench to bedside