Disparity map generation based on trapezoidal camera architecture for multiview video

Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities, the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video remains a huge challenge. This paper presents the mathematical description of trapezoidal camera architecture and relationships which facilitate the determination of camera position for visual content acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera Architecture is that it allows for adaptive camera topology by which points within the scene, especially the occluded ones can be optically and geometrically viewed from several different viewpoints either on the edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate description could very well be used to address the issue of occlusion which continues to be a major problem in computer vision with regards to the generation of depth map

SUPER MULTI-VIEW NEAR-EYE DISPLAY WITH LED ARRAY AND WAVEGUIDE ILLUMINATION MODULE

A near-eye display includes an array of light sources, a reflective spatial light modulator (SLM) synchronized with the array of light sources and configured to modulate and reflect incident light beams to generate images, display optics configured to project the images generated by the reflective SLM to a user’s eye, and a waveguide between the display optics and the reflective SLM, where the waveguide is configured to guide light beams emitted by the array of light sources and direct the light beams towards the reflective SLM to illuminate the reflective SL

Advanced Calibration of Automotive Augmented Reality Head-Up Displays = Erweiterte Kalibrierung von Automotiven Augmented Reality-Head-Up-Displays

In dieser Arbeit werden fortschrittliche Kalibrierungsmethoden für Augmented-Reality-Head-up-Displays (AR-HUDs) in Kraftfahrzeugen vorgestellt, die auf parametrischen perspektivischen Projektionen und nichtparametrischen Verzerrungsmodellen basieren. Die AR-HUD-Kalibrierung ist wichtig, um virtuelle Objekte in relevanten Anwendungen wie z.B. Navigationssystemen oder Parkvorgängen korrekt zu platzieren. Obwohl es im Stand der Technik einige nützliche Ansätze für dieses Problem gibt, verfolgt diese Dissertation das Ziel, fortschrittlichere und dennoch weniger komplizierte Ansätze zu entwickeln. Als Voraussetzung für die Kalibrierung haben wir mehrere relevante Koordinatensysteme definiert, darunter die dreidimensionale (3D) Welt, den Ansichtspunkt-Raum, den HUD-Sichtfeld-Raum (HUD-FOV) und den zweidimensionalen (2D) virtuellen Bildraum. Wir beschreiben die Projektion der Bilder von einem AR-HUD-Projektor in Richtung der Augen des Fahrers als ein ansichtsabhängiges Lochkameramodell, das aus intrinsischen und extrinsischen Matrizen besteht. Unter dieser Annahme schätzen wir zunächst die intrinsische Matrix unter Verwendung der Grenzen des HUD-Sichtbereichs. Als nächstes kalibrieren wir die extrinsischen Matrizen an verschiedenen Blickpunkten innerhalb einer ausgewählten "Eyebox" unter Berücksichtigung der sich ändernden Augenpositionen des Fahrers. Die 3D-Positionen dieser Blickpunkte werden von einer Fahrerkamera verfolgt. Für jeden einzelnen Blickpunkt erhalten wir eine Gruppe von 2D-3D-Korrespondenzen zwischen einer Menge Punkten im virtuellen Bildraum und ihren übereinstimmenden Kontrollpunkten vor der Windschutzscheibe. Sobald diese Korrespondenzen verfügbar sind, berechnen wir die extrinsische Matrix am entsprechenden Betrachtungspunkt. Durch Vergleichen der neu projizierten und realen Pixelpositionen dieser virtuellen Punkte erhalten wir eine 2D-Verteilung von Bias-Vektoren, mit denen wir Warping-Karten rekonstruieren, welche die Informationen über die Bildverzerrung enthalten. Für die Vollständigkeit wiederholen wir die obigen extrinsischen Kalibrierungsverfahren an allen ausgewählten Betrachtungspunkten. Mit den kalibrierten extrinsischen Parametern stellen wir die Betrachtungspunkte wieder her im Weltkoordinatensystem. Da wir diese Punkte gleichzeitig im Raum der Fahrerkamera verfolgen, kalibrieren wir weiter die Transformation von der Fahrerkamera in den Weltraum unter Verwendung dieser 3D-3D-Korrespondenzen. Um mit nicht teilnehmenden Betrachtungspunkten innerhalb der Eyebox umzugehen, erhalten wir ihre extrinsischen Parameter und Warping-Karten durch nichtparametrische Interpolationen. Unsere Kombination aus parametrischen und nichtparametrischen Modellen übertrifft den Stand der Technik hinsichtlich der Zielkomplexität sowie Zeiteffizienz, während wir eine vergleichbare Kalibrierungsgenauigkeit beibehalten. Bei allen unseren Kalibrierungsschemen liegen die Projektionsfehler in der Auswertungsphase bei einer Entfernung von 7,5 Metern innerhalb weniger Millimeter, was einer Winkelgenauigkeit von ca. 2 Bogenminuten entspricht, was nahe am Auflösungvermögen des Auges liegt

Geometric Inference with Microlens Arrays

This dissertation explores an alternative to traditional fiducial markers where geometric information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation of markers in an image. We present markers fabricated from microlenses whose appearance changes depending on the marker\u27s orientation relative to the camera. First, we show how to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality

Smart cmos image sensor for 3d measurement

3D measurements are concerned with extracting visual information from the geometry of visible surfaces and interpreting the 3D coordinate data thus obtained, to detect or track the position or reconstruct the profile of an object, often in real time. These systems necessitate image sensors with high accuracy of position estimation and high frame rate of data processing for handling large volumes of data. A standard imager cannot address the requirements of fast image acquisition and processing, which are the two figures of merit for 3D measurements. Hence, dedicated VLSI imager architectures are indispensable for designing these high performance sensors. CMOS imaging technology provides potential to integrate image processing algorithms on the focal plane of the device, resulting in smart image sensors, capable of achieving better processing features in handling massive image data. The objective of this thesis is to present a new architecture of smart CMOS image sensor for real time 3D measurement using the sheet-beam projection methods based on active triangulation. Proposing the vision sensor as an ensemble of linear sensor arrays, all working in parallel and processing the entire image in slices, the complexity of the image-processing task shifts from O (N 2 ) to O (N). Inherent also in the design is the high level of parallelism to achieve massive parallel processing at high frame rate, required in 3D computation problems. This work demonstrates a prototype of the smart linear sensor incorporating full testability features to test and debug both at device and system levels. The salient features of this work are the asynchronous position to pulse stream conversion, multiple images binarization, high parallelism and modular architecture resulting in frame rate and sub-pixel resolution suitable for real time 3D measurements

Compact microscopy systems with non-conventional optical techniques

This work has been motivated by global efforts to decentralize high performance imaging systems through frugal engineering and expansion of 3D fabrication technologies. Typically, high resolution imaging systems are confined in clinical or laboratory environment due to the limited means of producing optical lenses on the demand. The use of lenses is an essential mean to achieve high resolution imaging, but conventional optical lenses are made using either polished glass or molded plastics. Both are suited for highly skilled craftsmen or factory level production. In the first part of this work, alternative low-cost lens-making process for generating high quality optical lenses with minimal operator training have been discussed. We evoked the use of liquid droplets to make lenses. This unconventional method relies on interfacial forces to generate curved droplets that if solidified can become convex-shaped lenses. To achieve this, we studied the droplet behaviour (Rayleigh-Plateau phenomenon) before creating a set of 3D printed tools to generate droplets. We measured and characterized the fabrication techniques to ensure reliability in lens fabrication on- demand at high throughput. Compact imaging requires a compact optical system and computing unit. So, in the next part of this work, we engineered a deconstructed microscope system for field-portable imaging. Still a core limitation of all optical lenses is the physical size of lens aperture – which limits their resolution performance, and optical aberrations – that limit their imaging quality performance. In the next part of this work, we investigated use of computational optics-based optimization approaches to conduct in situ characterization of aberrations that can be digitally removed. The computational approach we have used in this work is known as Fourier Ptychography (FP). It is an emerging computational microscopic technique that combines the use of synthetic aperture and iterative optimization algorithms, offering increased resolution, at full field-of-view (FOV) and aberration-removal. In using FP techniques, we have shown measurements of optical distortions from different lenses made from droplets only. We also, investigated the limitations of FP in aberration recovery on moldless lenses. In conclusion, this work presents new opportunities to engineer high resolution imaging system using modern 3D printing approaches. Our successful demonstration of FP techniques on moldless lenses will usher new additional applications in digital pathology or low-cost mobile health

Single-photon detection techniques for underwater imaging

This Thesis investigates the potential of a single-photon depth profiling system for imaging in highly scattering underwater environments. This scanning system measured depth using the time-of-flight and the time-correlated single-photon counting (TCSPC) technique. The system comprised a pulsed laser source, a monostatic scanning transceiver, with a silicon single-photon avalanche diode (SPAD) used for detection of the returned optical signal. Spectral transmittance measurements were performed on a number of different water samples in order to characterize the water types used in the experiments. This identified an optimum operational wavelength for each environment selected, which was in the wavelength region of 525 - 690 nm. Then, depth profiles measurements were performed in different scattering conditions, demonstrating high-resolution image re-construction for targets placed at stand-off distances up to nine attenuation lengths, using average optical power in the sub-milliwatt range. Depth and spatial resolution were investigated in several environments, demonstrating a depth resolution in the range of 500 μm to a few millimetres depending on the attenuation level of the medium. The angular resolution of the system was approximately 60 μrad in water with different levels of attenuation, illustrating that the narrow field of view helped preserve spatial resolution in the presence of high levels of forward scattering. Bespoke algorithms were developed for image reconstruction in order to recover depth, intensity and reflectivity information, and to investigate shorter acquisition times, illustrating the practicality of the approach for rapid frame rates. In addition, advanced signal processing approaches were used to investigate the potential of multispectral single-photon depth imaging in target discrimination and recognition, in free-space and underwater environments. Finally, a LiDAR model was developed and validated using experimental data. The model was used to estimate the performance of the system under a variety of scattering conditions and system parameters