This study investigates whether different stereo techniques used as a main array influences the preferred level from spot microphones when combined in a mix. Time delay compensation and its influence on spot microphone level was also examined. A clarinet soloist and a violin & piano duo were recorded as stimuli. A listening test was conducted where subjects were asked to set the level on spot microphone channels of a clarinet, and violin in combination with several stereo techniques. A/B, X/Y, ORTF, and Blumlein were examined. In general, results suggested that there wasn’t a significant difference in preferred spot microphone level between stereo techniques. Time delay compensation could not be proven to significantly influence the preferred spot microphone level.

For safe mobility, an autonomous vehicle must perceive the surroundings accurately. There are many perception tasks associated with understanding the local environment such as object detection, localization, and lane analysis. Object detection, in particular, plays a vital role in determining an object’s location and classifying it correctly and is one of the challenging tasks in the self-driving research area. Before employing an object detection module in autonomous vehicle testing, an organization needs to have a precise analysis of the module. Hence, it becomes crucial for a company to have an evaluation framework to evaluate an object detection algorithm’s performance. This thesis develops a comprehensive framework for evaluating and analyzing object detection algorithms, both 2D (camera images based) and 3D (LiDAR point cloud-based). The pipeline developed in this thesis provides the ability to evaluate multiple models with ease, signified by the key performance metrics, Average Precision, F-score, and Mean Average Precision. 40-point interpolation method is used to calculate the Average Precision. / För säker rörlighet måste ett autonomt fordon uppfatta omgivningen exakt. Det finns många uppfattningsuppgifter associerade med att förstå den lokala miljön, såsom objektdetektering, lokalisering och filanalys. I synnerhet objektdetektering spelar en viktig roll för att bestämma ett objekts plats och klassificera det korrekt och är en av de utmanande uppgifterna inom det självdrivande forskningsområdet. Innan en anställd detekteringsmodul används i autonoma fordonsprovningar måste en organisation ha en exakt analys av modulen. Därför blir det avgörande för ett företag att ha en utvärderingsram för att utvärdera en objektdetekteringsalgoritms prestanda. Denna avhandling utvecklar ett omfattande ramverk för utvärdering och analys av objektdetekteringsalgoritmer, både 2 D (kamerabilder baserade) och 3 D (LiDAR-punktmolnbaserade). Rörledningen som utvecklats i denna avhandling ger möjlighet att enkelt utvärdera flera modeller, betecknad med nyckelprestandamätvärdena, Genomsnittlig precision, F-poäng och genomsnittlig genomsnittlig precision. 40-punkts interpoleringsmetod används för att beräkna medelprecisionen.

Voiced communication plays a fundamental role in society. Voice research seeks to improve understanding of the fundamental physics governing voice production, with the eventual goal of improving methods to diagnose and treat voice disorders. For this thesis, three different aspects of voice production research were studied. First, porcine vocal fold medial surface geometry was determined, and the three-dimensional geometric distortion induced by freezing the larynx, especially in the region of the vocal folds, was quantified. It was found that porcine vocal folds are qualitatively geometrically similar to canine and human vocal folds, as well as commonly used models, and that freezing of tissue in the larynx causes distortion of around 5%. Second, a setup of multiple high-resolution cameras and a stereo-endoscopy system simultaneously recorded positions on the superior surface of synthetic, self-oscillating vocal fold models to estimate the error in the measurement of the three-dimensional location by the stereo-endoscopy system. The error was found to be low in the transverse plane, whereas the error was relatively large in the inferior-superior direction, suggesting that the stereo-endoscope is applicable for in vivo measurements of absolute distances of the glottis in the transverse plane such as glottal length, width, and area. Third, a function for strain-varying Poisson's ratio for silicone was developed from experimental data. It is anticipated that the findings herein can aid voice researchers as they study voice production, leading to improved voice care.

Stereo vision is a very useful, yet challenging technology for a wide variety of applications. One of the greatest challenges is meeting the computational demands of stereo vision applications that require real-time performance. The FPGA (Field Programmable Gate Array) is a readily-available technology that allows many stereo vision methods to be implemented while meeting the strict real-time performance requirements of some applications. Some of the best results have been obtained using non-parametric stereo correlation methods, such as the rank and census transform. Yet relatively little work has been done to study these methods or to propose new algorithms based on the same principles for improved stereo correlation accuracy or reduced resource requirements. This dissertation describes the sparse census and sparse rank transforms, which significantly reduce the cost of implementation while maintaining and in some case improving correlation accuracy. This dissertation also proposes the generalized census and generalized rank transforms, which opens up a new class of stereo vision transforms and allows the stereo system to be even more optimized, often reducing the hardware resource requirements. The proposed stereo methods are analyzed, providing both quantitative and qualitative results for comparison to existing algorithms. These results show that the computational complexity of local stereo methods can be significantly reduced while maintaining very good correlation accuracy. A hardware architecture for the implementation of the proposed algorithms is also described and the actual resource requirements for the algorithms are presented. These results confirm that dramatic reductions in hardware resource requirements can be achieved while maintaining high stereo correlation accuracy. This work proposes the multi-bit census, which provides improved pixel discrimination as compared to the census, and leads to improved correlation accuracy with some stereo configurations. A rotation-invariant census transform is also proposed and can be used in applications where image rotation is possible.

GPS/IMU integrated systems are commonly used for vehicle navigation. The algorithm for this coupled system is normally based on Kalman filter. However, oscillated GPS measurements in the urban environment can lead to localization divergence easily. Moreover, heading estimation may be sensitive to magnetic interference if it relies on IMU with integrated magnetometer. This report tries to solve the localization problem on GPS oscillation and outage, based on adaptive extended Kalman filter(AEKF). In terms of the heading estimation, stereo visual odometry(VO) is fused to overcome the effect by magnetic disturbance. Vision-aided AEKF based algorithm is tested in the cases of both good GPS condition and GPS oscillation with magnetic interference. Under the situations considered, the algorithm is verified to outperform conventional extended Kalman filter(CEKF) and unscented Kalman filter(UKF) in position estimation by 53.74% and 40.09% respectively, and decrease the drifting of heading estimation. / GPS/IMU integrerade system används ofta för navigering av fordon. Algoritmen för detta kopplade system är normalt baserat på ett Kalmanfilter. Ett problem med systemet är att oscillerade GPS mätningar i stadsmiljöer enkelt kan leda till en lokaliseringsdivergens. Dessutom kan riktningsuppskattningen vara känslig för magnetiska störningar om den är beroende av en IMU med integrerad magnetometer. Rapporten försöker lösa lokaliseringsproblemet som skapas av GPS-oscillationer och avbrott med hjälp av ett adaptivt förlängt Kalmanfilter (AEKF). När det gäller riktningsuppskattningen används stereovisuell odometri (VO) för att försvaga effekten av magnetiska störningar genom sensorfusion. En Visionsstödd AEKF-baserad algoritm testas i fall med både goda GPS omständigheter och med oscillationer i GPS mätningar med magnetiska störningar. Under de fallen som är aktuella är algoritmen verifierad för att överträffa det konventionella utökade Kalmanfilteret (CEKF) och ”Unscented Kalman filter” (UKF) när det kommer till positionsuppskattning med 53,74% respektive 40,09% samt minska fel i riktningsuppskattningen.

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of a selection of stereo depth estimation algorithms is compared to the performance of two triple-camera depth estimation algorithms: disparity fusion and cost fusion. The cost fusion algorithm in both a multi-baseline and multi-axis triple-camera configuration outperformed the environmental mapping accuracy of non-CNN algorithms in a two-camera configuration.

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Recent years have witnessed tremendous success in using deep learning approaches to handle low-level vision problems. Most of the deep learning based methods address the low-level vision problem by training a neural network to approximate the mapping from the inputs to the desired ground truths. However, directly learning this mapping is usually difficult and cannot achieve ideal performance. Besides, under the setting of unsupervised learning, the general deep learning approach cannot be used. In this thesis, we investigate and address several problems in low-level vision using the proposed approaches. To learn a better mapping using the existing data, an indirect domain shift mechanism is proposed to add explicit constraints inside the neural network for single image dehazing. This allows the neural network to be optimized across several identified neighbours, resulting in a better performance. Despite the success of the proposed approaches in learning an improved mapping from the inputs to the targets, three problems of unsupervised learning is also investigated. For unsupervised monocular depth estimation, a teacher-student network is introduced to strategically integrate both supervised and unsupervised learning benefits. The teacher network is formed by learning under the binocular depth estimation setting, and the student network is constructed as the primary network for monocular depth estimation. In observing that the performance of the teacher network is far better than that of the student network, a knowledge distillation approach is proposed to help improve the mapping learned by the student. For single image dehazing, the current network cannot handle different types of haze patterns as it is trained on a particular dataset. The problem is formulated as a multi-domain dehazing problem. To address this issue, a test-time training approach is proposed to leverage a helper network in assisting the dehazing network adapting to a particular domain using self-supervision. In lossy compression system, the target distribution can be different from that of the source and ground truths are not available for reference. Thus, the objective is to transform the source to target under a rate constraint, which generalizes the optimal transport. To address this problem, theoretical analyses on the trade-off between compression rate and minimal achievable distortion are studied under the cases with and without common randomness. A deep learning approach is also developed using our theoretical results for addressing super-resolution and denoising tasks. Extensive experiments and analyses have been conducted to prove the effectiveness of the proposed deep learning based methods in handling the problems in low-level vision. / Thesis / Doctor of Philosophy (PhD)

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