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Abstract Introduction: H.J.-Haase developed a systematic set of tests for evaluating binocular vision called the Pola Test. The Pola Test measures associated phoria and stereoacuity at distance and near using a variety of different targets for each. This testing method and interpretation is referred to as MKH-Haase (Measuring and Correcting Methodology after H.J.Haase ???the MKH) method. The MKH method is more commonly used in Germany and other European countries than English speaking countries. The MKH-Haase method has been considered a reliable method for prescribing prisms to symptomatic binocular vision patients. Purpose: To investigate the test-retest reliability of binocular vision measurements using the MKH-Haase series of tests that comprise the Pola Test. In addition, I will compare the Pola results with other associated phoria and stereoacuity tests used in North America. Methods: Thirty-four symptomatic and 40 asymptomatic subjects (based on a symptoms questionnaire) participated in this study. Associated phoria and stereoacuity with different tests, including the Pola Test at distance and near, were measured for those subjects on two different sessions. Not all of subjects were tested with all tests. Only 30 subjects in each group completed all of tests. The Pola Test protocol requires the associated phoria and stereoacuity to be measured twice within a session; once with the Polariods oriented with their axes at 45o and 135o and again with the axes switched. Results: Within and between-sessions repeatability of MKH-Haase associated phoria and stereoacuity tests results revealed that most of MKH-Haase associated phoria and stereoacuity tests showed good repeatability within and between-sessions at both distance and near. However, there were a few exceptions to this general finding. Distance horizontal associated phoria values for the Cross Test and Pointer Test at the first session, and the distance Double Pointer Test values at the second session showed some differences between the two views. Between-sessions repeatability of the associated phoria tests did not show any significant differences. For the stereoacuity tests, the differences between the two disparities were statistically significant at the first session for the symptomatic group Line Test and asymptomatic group Step Test. For the second session at distance, the differences were significant with Step Test for both groups. The differences between sessions for both disparities were not significant for most of tests. The symptomatic group???s Step Test for crossed disparity and asymptomatic group's Step Test for uncrossed disparity were exceptions. A repeated measures ANOVA test was conducted to compare different associated phoria tests. Horizontal associated phoria tests without central fusion lock were significantly different from those with central fusion lock at distance and near. Comparison of different stereoacuity tests was conducted by comparing the number of subjects who could identify specific stereothreshold values. Results showed that at both distance and near, there were no significant differences between contour and global stereoacuity tests based on number of subjects who could attain 60 sec of arc or better. Discussion and Conclusion: Most of MKH-Haase associated phoria and stereoacuity charts have reasonable within and between-sessions repeatability. However, some associated phoria tests showed some differences especially with subjects who had higher values. Although there was a significant difference between various horizontal associated phoria tests at distance and near, most of the values differed by around 0.50 ???. The exception was the difference between the Wesson Card and Disparometer. The Wesson card was more exo by 1.50 ??? than the Disparometer. Vertical associated phoria tests did not show any significant differences. Although MKH-Haase chart can measure local stereothreshold down to 10 sec of arc at distance, the AO Slide is easier to perceive. Random dot stereoacuity can be measured with MKH-Haase charts at distance down to 30 sec of arc. All of the contour stereoacuity tests are comparable at near. However, the MKH-Haase chart was easier to perceive. The Random Dot Randot test would be more useful for fast screening purposes. Random dot MKH-Haase test would be easier than TNO Test to measure random dot stereothreshold at near.

Three-dimensional information is often used in robotics and 3D-mapping. There exist several ways to obtain a three-dimensional map. However, the time of flight used in the laser scanners or the structured light utilized by Kinect-like sensors sometimes are not sufficient. In this thesis, we investigate two CNN based stereo matching methods for obtaining 3D-information from a grayscaled pair of rectified images.While the state-of-the-art stereo matching method utilize a Siamese architecture, in this project a two-channel and a two stream network are trained in an attempt to outperform the state-of-the-art. A set of experiments were performed to achieve optimal hyperparameters. By changing one parameter at the time, the networks with architectures mentioned above are trained. After a completed training the networks are evaluated with two criteria, the error rate, and the runtime.Due to time limitations, we were not able to find optimal learning parameters. However, by using settings from [17] we train a two-channel network that performed almost on the same level as the state-of-the-art. The error rate on the test data for our best architecture is 2.64% while the error rate for the state-of-the-art Siamese network is 2.62%. We were not able to achieve better performance than the state-of-the-art, but we believe that it is possible to reduce the error rate further. On the other hand, the state-of-the-art Siamese stereo matching network is more efficient and faster during the disparity estimation. Therefore, if the time efficiency is prioritized, the Siamese based network should be considered.

This thesis explores and includes research on different impressions given in stereo rendering when creating a fire in three different manners.As technology progresses, stereoscopic rendering becomes increasingly more popular. Both movies and games are shown through VR to give users a more satisfying and lifelike interaction with a virtual environment.Three different versions of a fire were created in a game engine and then viewed with HTC Vive. Comparing mono and stereo rendering gave insights on how the different method looks when using stereoscopic rendering, and what impression the different methods gave the user.The different fires were tested with several users. Finally, this thesis concludes with an interview to one of the users and his different impressions of the fires. / Denna rapport undersöker och innehåller forskning om olika uppfattningar som uppkommer i stereo rendering när eld skapas på tre olika metoder.Allteftersom tekniken utvecklas blir stereo rendering allt mer populär. Både filmer och spel visas i VR för att ge användaren en mer tillfredsställande och verklighetstrogen upplevelse i en virtuell miljö.Tre olika versioner av eld skapades i en spelmotor och granskades sedan via en HTC Vive. Jämförandet av mono och stereo rendering gav en insikt i hur de olika metoderna uppvisas med användning av stereo rendering, och vilka uppfattningar de olika metoderna gav användaren.De olika eldarna testades av flera användare. Tillslut, avslutades denna tes med en intervju med en av användarna och hans uppfattningar av eldarna.

For a mobile robot to engage in exploration of a-priori unknown environments it must be able to identify locations which will yield new information when visited. This thesis presents two novel algorithms which attempt to answer the question of choosing where a robot should go next in a partially explored workspace. To begin we describe the process of acquiring highly accurate dense 3D data from a stereo camera. This approach combines techniques from a number of existing implementations and is demonstrated to be more accurate than a range of commercial offerings. Combined with state of the art visual odometry based pose estimation we can use these point clouds to drive exploration. The first exploration algorithm we present is an attempt to represent the three dimensional world as a continuous two dimensional surface. The surface is maintained as a planar graph structure in which vertices correspond to points in space as seen by the stereo camera. Edges connect vertices which have been seen as adjacent pixels in a stereo image pair, and have a weight equal to the Euclidean distance between the end points. Discontinuities in the input stereo data manifest as areas of the graph with high average edge weight, and by moving the camera to view such areas and merging the new scan with the existing graph, we push back the boundary of the explored workspace. Motivated by scaling and precision problems with the graph-based method, we present a second exploration algorithm based on continuum methods. We show that by solving Laplace’s equation over the freespace of the partially explored environment, we can guide exploration by following streamlines in the resulting vector field. Choosing appropriate boundary conditions ensures that these streamlines run parallel to obstacles and are guaranteed to lead to a frontier – a boundary between explored and unexplored space. Results are shown which demonstrate this method fully exploring three dimensional environments and outperforming oft-used information gain based approaches. We show how analysis of the potential field solution can be used to identify volumes of the workspace which have been fully explored, thus reducing future computation.

This thesis investigates techniques and designs an autonomous visual stereo based navigation sensor to improve stereo visual odometry for purpose of navigation in unknown environments. In particular, autonomous navigation in a space mission context which imposes challenging constraints on algorithm development and hardware requirements. For instance, Global Positioning System (GPS) is not available in this context. Thus, a solution for navigation cannot rely on similar external sources of information. Support to handle this problem is required with the conception of an intelligent perception-sensing device that provides precise outputs related to absolute and relative 6 degrees of freedom (DOF) positioning. This is achieved using only images from stereo calibrated cameras possibly coupled with an inertial measurement unit (IMU) while fulfilling real time processing requirements. Moreover, no prior knowledge about the environment is assumed. Robotic navigation has been the motivating research to investigate different and complementary areas such as stereovision, visual motion estimation, optimisation and data fusion. Several contributions have been made in these areas. Firstly, an efficient feature detection, stereo matching and feature tracking strategy based on Kanade-Lucas-Tomasi (KLT) feature tracker is proposed to form the base of the visual motion estimation. Secondly, in order to cope with extreme illumination changes, High dynamic range (HDR) imaging solution is investigated and a comparative assessment of feature tracking performance is conducted. Thirdly, a two views local bundle adjustment scheme based on trust region minimisation is proposed for precise visual motion estimation. Fourthly, a novel KLT feature tracker using IMU information is integrated into the visual odometry pipeline. Finally, a smart standalone stereo visual/IMU navigation sensor has been designed integrating an innovative combination of hardware as well as the novel software solutions proposed above. As a result of a balanced combination of hardware and software implementation, we achieved 5fps frame rate processing up to 750 initials features at a resolution of 1280x960. This is the highest reached resolution in real time for visual odometry applications to our knowledge. In addition visual odometry accuracy of our algorithm achieves the state of the art with less than 1% relative error in the estimated trajectories.

Esta tese visa apresentar uma contribuição ao desenvolvimento de sistemas computacionais, baseados principalmente em visão computacional, usados para o auxílio a navegação de robôs móveis e veículos inteligentes. Inicialmente, buscou-se apresentar uma proposição de uma arquitetura de um sistema computacional para veículos inteligente que permita a construção de sistemas que sirvam tanto para o apoio ao motorista, auxiliando-o em sua forma de condução, quanto para o controle autônomo, proporcionando maior segurança e autonomia do tráfego de veículos em meio urbano, em rodovias e inclusive no meio rural. Esta arquitetura vem sendo aperfeiçoada e validada junto as plataformas CaRINA I e CaRINA II (Carro Robótico Inteligente para Navegação Autônoma), que também foram alvo de desenvolvimentos e pesquisas junto a esta tese, permitindo também a experimentação prática dos conceitos propostos nesta tese. Neste contexto do desenvolvimento de veículos inteligentes e autônomos, o uso de sensores para a percepção 3D do ambiente possui um papel muito importante, permitindo o desvio de obstáculos e navegação autônoma, onde a adoção de sensores de menor custo tem sido buscada a m de viabilizar aplicações comerciais. As câmeras estéreo são dispositivos que se enquadram nestes requisitos de custo e percepção 3D, destacando-se como sendo o foco da proposta de um novo método automático de calibração apresentado nesta tese. O método proposto permite estimar os parâmetros extrínsecos de um sistema de câmeras estéreo através de um processo evolutivo que considera apenas a coerência e a qualidade de alguns elementos do cenário quanto ao mapa de profundidade. Esta proposta apresenta uma forma original de calibração que permite a um usuário, sem grandes conhecimentos sobre visão estéreo, ajustar o sistema de câmeras para novas configurações e necessidades. O sistema proposto foi testado com imagens reais, obtendo resultados bastante promissores, se comparado aos métodos tradicionais de calibração de câmeras estéreo que fazem uso de um processo interativo de estimação dos parâmetros através da apresentação e uso de um padrão xadrez. Este método apresenta-se como uma abordagem promissora para realizar a fusão dos dados de câmeras e sensores, permitindo o ajuste das matrizes de transformação (parâmetros extrínsecos do sistema), a m de obter uma referência única onde são representados e agrupados os dados vindos dos diferentes sensores. / This thesis aims to provide a contribution to computer systems development based on computer vision used to aid the navigation of mobile robots and intelligent vehicles. Initially, we propose a computer system architecture for intelligent vehicles where intention is to support both the driver, helping him in driving way; and the autonomous control, providing greater security and autonomy of vehicular traffic in urban areas, on highways and even in rural areas. This architecture has been validated and improved with CaRINA I and CaRINA II platforms development, which were also subject of this thesis and allowed the practical experimentation of concepts proposed. In context of intelligent autonomous vehicles, the use of sensors that provides a 3D environment perception has a very important role to enable obstacle avoidance and autonomous navigation. Therefor the adoption of lower cost sensors have been sought in order to facilitate commercial applications. The stereo cameras are devices that fit these both requirements (cost and 3D perception), standing out as focus of the proposal for a new automatic calibration method presented in this thesis. The proposed method allows to estimate the extrinsic parameters of a stereo camera system through an evolutionary process that considers only the consistency and the quality of some elements of the scenario as to the depth map. This proposal presents a unique form of calibration that allows a user without much knowledge of stereo vision, adjust the camera system for new settings and needs. The system was tested with real images, obtaining very promising results as compared to traditional methods of calibration of stereo cameras that use an iterative process of parameter estimation through the presentation and use of a checkerboard pattern. This method offers a promising approach to achieve the fusion of the data from cameras and sensors, allowing adjustment of transformation matrices (extrinsic system parameters) in order to obtain a single reference in which they are grouped together and represented the data from the different sensors.

Este trabalho apresenta um procedimento de estimação dos parâmetros de movimento de um corpo rígido, quais sejam, posição, velocidade linear, velocidade angular e aceleração linear, empregando técnicas de visão computacional fundamentadas na manipulação de sequências de imagens estéreo ruidosas. A estimação envolve o uso de detetares de cantos, observação de pontos ruidosos tridimensionais, aquisição e processamento de imagens e estimação de estados. Atenção especial é dada às duas primeiras técnicas pois é a partir delas que os atributos geométricos do objeto são adquiridos durante o processamento de imagens digitalizadas. O processo de recuperação de pontos tridimensionais foi simulado a partir de informações bidimensionais, usando dois métodos de observação de pontos ruidosos. Um desses métodos reduz o problema de determinação dos pontos tridimensionais à resolução de um sistema de equações lineares pelo método dos mínimos quadrados. O outro utiliza informação do range de um ponto imagem e da correspondência entre um ponto projetado em uma câmera e o correspondente ponto conjugado, localizado na outra câmera. Ainda neste trabalho, técnicas aplicáveis na resolução do problema da correspondência entre imagens estéreos foram estudadas. Duas abordagens são consideradas. Em uma delas, o estudo da correspondência é feito a partir da manipulação de imagens segmentadas e na outra utiliza-se as imagens adquiridas. Um experimento empregando o procedimento de estimação dos parâmetros obtido foi realizado. O experimento consistiu no deslocamento de um corpo rígido em uma mesa e na aquisição de uma seqüência de imagens estéreo. As estimativas obtidas foram comparadas com os valores dos parâmetros de movimento observados experimentalmente. / This work presents a procedure for the estimation of object motion parameters, namely, position, linear velocity, angular velocity and linear acceleration, using computer vision techniques based on manipulation of a sequence of noisy stereo images. The estimation involves corner detection, 3-D noisy point observation, image acquisition, digital image processing and state estimates. Special attention was given to the first two techniques mentioned above because the acquisition of the object features during the image processing was based on them. The process of recovering three dimensional points was simulated from the 2-D information, using two methods based on noisy points observation. One of these methods formulates the problem of determination of the three-dimensional points as a linear system equation problem via least square techniques. The other solves the same problem by using the definition of the range of image points and the correspondence that exists between a projected point on one camera and the correspondent conjugate point on the other camera. We still made in this work a study of techniques dedicated to solve the problem of stereo images correspondence. Two approaches are examined. In one of them, the correspondence is made from segmented images. In the other one, the acquired images are used directly. An experiment using the parameter estimate procedure was performed. The experiment consisted in observing a moving object on a test table and in acquiring a stereo image sequence. The estimates were compared with the motion parameters experimentally observed.

Les applications basées sur 3D tels que les films 3D, l’impression 3D, la cartographie 3D, la reconnaissance 3D, sont de plus en plus présentes dans notre vie quotidienne; elles exigent une reconstruction 3D qui apparaît alors comme une technique clé. Dans cette thèse, nous nous intéressons à l’appariement stéréo qui est au coeur de l’acquisition 3D. Malgré les nombreuses publications traitant de l’appariement stéréo, il demeure un défi en raison des contraintes de précision et de temps de calcul: la conduite autonome requiert le temps réel; la modélisation d’objets 3D exige une précision et une résolution élevées. La méthode de pondération adaptative des pixels de support (adaptative-supportweight), basée sur le bien connu filtre bilatéral, est une méthode de l’état de l’art, de catégorie locale, qui en dépit de ses potentiels atouts peine à lever l’ambiguïté induite par des pixels voisins, de disparités différentes mais avec des couleurs similaires. Notre première contribution, à base de filtre trilatéral, est une solution pertinente qui tout en conservant les avantages du filtre bilatéral permet de lever l’ambiguïté mentionnée. Evaluée sur le corpus de référence, communément acceptée, Middlebury, elle se positionne comme étant la plus précise au moment où nous écrivons ces lignes. Malgré ces performances, la complexité de notre première contribution est élevée. Elle dépend en effet de la taille de la fenêtre support. Nous avons proposé alors une implémentation récursive du filtre trilatérale, inspirée par les filtres récursifs. Ici, les coûts bruts en chaque pixel sont agrégés à travers une grille organisée en graphe. Quatre passages à une dimension permettent d’atteindre une complexité en O(N), indépendante cette fois de la taille de la fenêtre support. C’est-à-dire des centaines de fois plus rapide que la méthode originale. Pour le calcul des pondérations des pixels du support, notre méthode basée sur le filtre trilatéral introduit un nouveau terme, qui est une fonction d’amplitude du gradient. Celui-ci est remarquable aux bords des objets, mais aussi en cas de changement de couleurs et de texture au sein des objets. Or, le premier cas est déterminant dans l’estimation de la profondeur. La dernière contribution de cette thèse vise alors à distinguer les contours des objets de ceux issus du changement de couleur au sein de l’objet. Les évaluations, sur Middlebury, prouvent l’efficacité de la méthode proposée. Elle est en effet plus précise que la méthode basée sur le filtre trilatéral d’origine, mais aussi d’autres méthodes locales. / 3D-related applications are becoming more and more popular in our daily life, such as 3D movies, 3D printing, 3D maps, 3D object recognition, etc. Many applications require realistic 3D models and thus 3D reconstruction is a key technique behind them. In this thesis, we focus on a basic problem of 3D reconstruction, i.e. stereo matching, which searches for correspondences in a stereo pair or more images of a 3D scene. Although various stereo matching methods have been published in the past decades, it is still a challenging task since the high requirement of accuracy and efficiency in practical applications. For example, autonomous driving demands realtime stereo matching technique; while 3D object modeling demands high quality solution. This thesis is dedicated to develop efficient and accurate stereo matching method. The well-known bilateral filter based adaptive support weight method represents the state-of-the-art local method, but it hardly sorts the ambiguity induced by nearby pixels at different disparities but with similar colors. Therefore, we proposed a novel trilateral filter based method that remedies such ambiguities by introducing a boundary strength term. As evaluated on the commonly accepted Middlebury benchmark, the proposed method is proved to be the most accurate local stereo matching method at the time of submission (April 2013). The computational complexity of the trilateral filter based method is high and depends on the support window size. In order to enhance its computational efficiency, we proposed a recursive trilateral filter method, inspired by recursive filter. The raw costs are aggregated on a grid graph by four one-dimensional aggregations and its computational complexity proves to be O(N), which is independent of the support window size. The practical runtime of the proposed recursive trilateral filter based method processing 375 _ 450 resolution image is roughly 260ms on a PC with a 3:4 GHz Inter Core i7 CPU, which is hundreds times faster than the original trilateral filter based method. The trilateral filter based method introduced a boundary strength term, which is computed from color edges, to handle the ambiguity induced by nearby pixels at different disparities but with similar colors. The color edges consist of two types of edges, i.e. depth edges and texture edges. Actually, only depth edges are useful for the boundary strength term. Therefore, we presented a depth edge detection method, aiming to pick out depth edges and proposed a depth edge trilateral filter based method. Evaluation on Middlebury benchmark proves the effectiveness of the proposed depth edge trilateral filter method, which is more accurate than the original trilateral filter method and other local stereo matching methods.

Esta tese visa apresentar uma contribuição ao desenvolvimento de sistemas computacionais, baseados principalmente em visão computacional, usados para o auxílio a navegação de robôs móveis e veículos inteligentes. Inicialmente, buscou-se apresentar uma proposição de uma arquitetura de um sistema computacional para veículos inteligente que permita a construção de sistemas que sirvam tanto para o apoio ao motorista, auxiliando-o em sua forma de condução, quanto para o controle autônomo, proporcionando maior segurança e autonomia do tráfego de veículos em meio urbano, em rodovias e inclusive no meio rural. Esta arquitetura vem sendo aperfeiçoada e validada junto as plataformas CaRINA I e CaRINA II (Carro Robótico Inteligente para Navegação Autônoma), que também foram alvo de desenvolvimentos e pesquisas junto a esta tese, permitindo também a experimentação prática dos conceitos propostos nesta tese. Neste contexto do desenvolvimento de veículos inteligentes e autônomos, o uso de sensores para a percepção 3D do ambiente possui um papel muito importante, permitindo o desvio de obstáculos e navegação autônoma, onde a adoção de sensores de menor custo tem sido buscada a m de viabilizar aplicações comerciais. As câmeras estéreo são dispositivos que se enquadram nestes requisitos de custo e percepção 3D, destacando-se como sendo o foco da proposta de um novo método automático de calibração apresentado nesta tese. O método proposto permite estimar os parâmetros extrínsecos de um sistema de câmeras estéreo através de um processo evolutivo que considera apenas a coerência e a qualidade de alguns elementos do cenário quanto ao mapa de profundidade. Esta proposta apresenta uma forma original de calibração que permite a um usuário, sem grandes conhecimentos sobre visão estéreo, ajustar o sistema de câmeras para novas configurações e necessidades. O sistema proposto foi testado com imagens reais, obtendo resultados bastante promissores, se comparado aos métodos tradicionais de calibração de câmeras estéreo que fazem uso de um processo interativo de estimação dos parâmetros através da apresentação e uso de um padrão xadrez. Este método apresenta-se como uma abordagem promissora para realizar a fusão dos dados de câmeras e sensores, permitindo o ajuste das matrizes de transformação (parâmetros extrínsecos do sistema), a m de obter uma referência única onde são representados e agrupados os dados vindos dos diferentes sensores. / This thesis aims to provide a contribution to computer systems development based on computer vision used to aid the navigation of mobile robots and intelligent vehicles. Initially, we propose a computer system architecture for intelligent vehicles where intention is to support both the driver, helping him in driving way; and the autonomous control, providing greater security and autonomy of vehicular traffic in urban areas, on highways and even in rural areas. This architecture has been validated and improved with CaRINA I and CaRINA II platforms development, which were also subject of this thesis and allowed the practical experimentation of concepts proposed. In context of intelligent autonomous vehicles, the use of sensors that provides a 3D environment perception has a very important role to enable obstacle avoidance and autonomous navigation. Therefor the adoption of lower cost sensors have been sought in order to facilitate commercial applications. The stereo cameras are devices that fit these both requirements (cost and 3D perception), standing out as focus of the proposal for a new automatic calibration method presented in this thesis. The proposed method allows to estimate the extrinsic parameters of a stereo camera system through an evolutionary process that considers only the consistency and the quality of some elements of the scenario as to the depth map. This proposal presents a unique form of calibration that allows a user without much knowledge of stereo vision, adjust the camera system for new settings and needs. The system was tested with real images, obtaining very promising results as compared to traditional methods of calibration of stereo cameras that use an iterative process of parameter estimation through the presentation and use of a checkerboard pattern. This method offers a promising approach to achieve the fusion of the data from cameras and sensors, allowing adjustment of transformation matrices (extrinsic system parameters) in order to obtain a single reference in which they are grouped together and represented the data from the different sensors.