Self-localization in urban environment via mobile imaging facility.

Chim, Ho Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 58-62).Abstracts in English and Chinese.Acknowledgements --- p.iAbstract --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives --- p.1Chapter 1.2 --- Motivations --- p.1Chapter 1.3 --- Problem Statement --- p.2Chapter 1.4 --- Camera Self-Localization Approaches --- p.3Chapter 1.4.1 --- Based on Calibration Patterns --- p.3Chapter 1.4.2 --- Based on Self-calibration --- p.3Chapter 1.4.3 --- Based on Shape and Motion --- p.4Chapter 1.4.4 --- The Proposed Approach - Based on Junctions --- p.5Chapter 1.5 --- Thesis Organization --- p.6Chapter 2 --- Previous Work --- p.7Chapter 2.1 --- Camera Self-Localization --- p.7Chapter 2.1.1 --- Parallel Plane Features --- p.7Chapter 2.1.2 --- Parallelepiped Features --- p.8Chapter 2.1.3 --- Single View Geometric Features --- p.8Chapter 2.1.4 --- Shape and Motion --- p.8Chapter 2.1.5 --- Other Estimation Methods --- p.9Chapter 2.2 --- Feature Correspondences Establishment --- p.9Chapter 2.2.1 --- Feature-based Object Recognition --- p.9Chapter 2.2.2 --- Model-based Object Recognition --- p.10Chapter 3 --- Preliminaries --- p.11Chapter 3.1 --- Perspective Camera Model --- p.11Chapter 3.2 --- Camera Pose from Point Correspondences --- p.15Chapter 3.3 --- Camera Pose from Direction Correspondences --- p.16Chapter 4 --- A Junction-based Approach --- p.18Chapter 4.1 --- Use of Junction Correspondences for Determining Camera Pose --- p.18Chapter 4.1.1 --- Constraints from Point Information --- p.19Chapter 4.1.2 --- Constraint from Direction Information --- p.21Chapter 4.1.3 --- Junction Triplet Correspondences --- p.22Chapter 4.2 --- Extraction of Junctions and Junction Triplets from Image --- p.24Chapter 4.2.1 --- Handling Image Data --- p.24Chapter 4.2.2 --- Bridging Lines --- p.25Chapter 4.2.3 --- """L""-junctions" --- p.26Chapter 4.2.4 --- """Y"" and ""Adjunctions" --- p.27Chapter 4.2.5 --- Junction Triplets --- p.28Chapter 4.3 --- Establishment of the First Junction Triplet Correspondence --- p.30Chapter 4.3.1 --- Ordered Junction Triplets from Model --- p.30Chapter 4.3.2 --- A Junction Hashing Scheme --- p.31Chapter 4.4 --- Establishment of Points Correspondence --- p.33Chapter 4.4.1 --- Viewing Sphere Tessellation --- p.33Chapter 4.4.2 --- Model Views Synthesizing --- p.35Chapter 4.4.3 --- Affine Coordinates Computation --- p.35Chapter 4.4.4 --- Hash Table Filling --- p.38Chapter 4.4.5 --- Hash Table Voting --- p.38Chapter 4.4.6 --- Hypothesis and Confirmation --- p.39Chapter 4.4.7 --- An Example of Geometric Hashing --- p.40Chapter 5 --- Experimental Results --- p.43Chapter 5.1 --- Results from Synthetic Image Data --- p.43Chapter 5.2 --- Results from Real Image Data --- p.45Chapter 5.2.1 --- Results on Laboratory Scenes --- p.46Chapter 5.2.2 --- Results on Outdoor Scenes --- p.48Chapter 6 --- Conclusion --- p.51Chapter 6.1 --- Contributions --- p.51Chapter 6.2 --- Advantages --- p.52Chapter 6.3 --- Summary and Future Work --- p.52Chapter A --- Least-Squares Method --- p.54Chapter B --- RQ Decomposition --- p.56Bibliography --- p.5

Grouping Uncertain Oriented Projective Geometric Entities with Application to Automatic Building Reconstruction

The fully automatic reconstruction of 3d scenes from a set of 2d images has always been a key issue in photogrammetry and computer vision and has not been solved satisfactory so far. Most of the current approaches match features between the images based on radiometric cues followed by a reconstruction using the image geometry. The motivation for this work is the conjecture that in the presence of highly redundant data it should be possible to recover the scene structure by grouping together geometric primitives in a bottom-up manner. Oriented projective geometry will be used throughout this work, which allows to represent geometric primitives, such as points, lines and planes in 2d and 3d space as well as projective cameras, together with their uncertainty. The first major contribution of the work is the use of uncertain oriented projective geometry, rather than uncertain projective geometry, that enables the representation of more complex compound entities, such as line segments and polygons in 2d and 3d space as well as 2d edgels and 3d facets. Within the uncertain oriented projective framework a procedure is developed, which allows to test pairwise relations between the various uncertain oriented projective entities. Again, the novelty lies in the possibility to check relations between the novel compound entities. The second major contribution of the work is the development of a data structure, specifically designed to enable performing the tests between large numbers of entities in an efficient manner. Being able to efficiently test relations between the geometric entities, a framework for grouping those entities together is developed. Various different grouping methods are discussed. The third major contribution of this work is the development of a novel grouping method that by analyzing the entropy change incurred by incrementally adding observations into an estimation is able to balance efficiency against robustness in order to achieve better grouping results. Finally the applicability of the proposed representations, tests and grouping methods for the task of purely geometry based building reconstruction from oriented aerial images is demonstrated. It will be shown that in the presence of highly redundant datasets it is possible to achieve reasonable reconstruction results by grouping together geometric primitives.Gruppierung unsicherer orientierter projektiver geometrischer Elemente mit Anwendung in der automatischen Gebäuderekonstruktion Die vollautomatische Rekonstruktion von 3D Szenen aus einer Menge von 2D Bildern war immer ein Hauptanliegen in der Photogrammetrie und Computer Vision und wurde bisher noch nicht zufriedenstellend gelöst. Die meisten aktuellen Ansätze ordnen Merkmale zwischen den Bildern basierend auf radiometrischen Eigenschaften zu. Daran schließt sich dann eine Rekonstruktion auf der Basis der Bildgeometrie an. Die Motivation für diese Arbeit ist die These, dass es möglich sein sollte, die Struktur einer Szene durch Gruppierung geometrischer Primitive zu rekonstruieren, falls die Eingabedaten genügend redundant sind. Orientierte projektive Geometrie wird in dieser Arbeit zur Repräsentation geometrischer Primitive, wie Punkten, Linien und Ebenen in 2D und 3D sowie projektiver Kameras, zusammen mit ihrer Unsicherheit verwendet.Der erste Hauptbeitrag dieser Arbeit ist die Verwendung unsicherer orientierter projektiver Geometrie, anstatt von unsicherer projektiver Geometrie, welche die Repräsentation von komplexeren zusammengesetzten Objekten, wie Liniensegmenten und Polygonen in 2D und 3D sowie 2D Edgels und 3D Facetten, ermöglicht. Innerhalb dieser unsicheren orientierten projektiven Repräsentation wird ein Verfahren zum testen paarweiser Relationen zwischen den verschiedenen unsicheren orientierten projektiven geometrischen Elementen entwickelt. Dabei liegt die Neuheit wieder in der Möglichkeit, Relationen zwischen den neuen zusammengesetzten Elementen zu prüfen. Der zweite Hauptbeitrag dieser Arbeit ist die Entwicklung einer Datenstruktur, welche speziell auf die effiziente Prüfung von solchen Relationen zwischen vielen Elementen ausgelegt ist. Die Möglichkeit zur effizienten Prüfung von Relationen zwischen den geometrischen Elementen erlaubt nun die Entwicklung eines Systems zur Gruppierung dieser Elemente. Verschiedene Gruppierungsmethoden werden vorgestellt. Der dritte Hauptbeitrag dieser Arbeit ist die Entwicklung einer neuen Gruppierungsmethode, die durch die Analyse der änderung der Entropie beim Hinzufügen von Beobachtungen in die Schätzung Effizienz und Robustheit gegeneinander ausbalanciert und dadurch bessere Gruppierungsergebnisse erzielt. Zum Schluss wird die Anwendbarkeit der vorgeschlagenen Repräsentationen, Tests und Gruppierungsmethoden für die ausschließlich geometriebasierte Gebäuderekonstruktion aus orientierten Luftbildern demonstriert. Es wird gezeigt, dass unter der Annahme von hoch redundanten Datensätzen vernünftige Rekonstruktionsergebnisse durch Gruppierung von geometrischen Primitiven erzielbar sind

Stereo Reconstruction using Induced Symmetry and 3D scene priors

Tese de doutoramento em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraRecuperar a geometria 3D a partir de dois vistas, conhecida como reconstrução estéreo, é um dos tópicos mais antigos e mais investigado em visão por computador. A computação de modelos 3D do ambiente é útil para uma grande número de aplicações, desde a robótica‎, passando pela sua utilização do consumidor comum, até a procedimentos médicos. O princípio para recuperar a estrutura 3D cena é bastante simples, no entanto, existem algumas situações que complicam consideravelmente o processo de reconstrução. Objetos que contêm estruturas pouco texturadas ou repetitivas, e superfícies com bastante inclinação ainda colocam em dificuldade os algoritmos state-of-the-art. Esta tese de doutoramento aborda estas questões e apresenta um novo framework estéreo que é completamente diferente das abordagens convencionais. Propomos a utilização de simetria em vez de foto-similaridade para avaliar a verosimilhança de pontos em duas imagens distintas serem uma correspondência. O framework é chamado SymStereo, e baseia-se no efeito de espelhagem que surge sempre que uma imagem é mapeada para a outra câmera usando a homografia induzida por um plano de corte virtual que intersecta a baseline. Experiências em estéreo denso comprovam que as nossas funções de custo baseadas em simetria se comparam favoravelmente com os custos baseados em foto-consistência de melhor desempenho. Param além disso, investigamos a possibilidade de realizar Stereo-Rangefinding, que consiste em usar estéreo passivo para recuperar exclusivamente a profundidade ao longo de um plano de varrimento. Experiências abrangentes fornecem evidência de que estéreo baseada em simetria induzida é especialmente eficaz para esta finalidade. Como segunda linha de investigação, propomos superar os problemas descritos anteriormente usando informação a priori sobre o ambiente 3D, com o objectivo de aumentar a robustez do processo de reconstrução. Para tal, apresentamos uma nova abordagem global para detectar pontos de desvanecimento e grupos de direcções de desvanecimento mutuamente ortogonais em ambientes Manhattan. Experiências quer em imagens sintéticas quer em imagens reais demonstram que os nossos algoritmos superaram os métodos state-of-the-art, mantendo a computação aceitável. Além disso, mostramos pela primeira vez resultados na detecção simultânea de múltiplas configurações de Manhattan. Esta informação a priori sobre a estrutura da cena é depois usada numa pipeline de reconstrução que gera modelos piecewise planares de ambientes urbanos a partir de duas vistas calibradas. A nossa formulação combina SymStereo e o algoritmo de clustering PEARL [3], e alterna entre um passo de otimização discreto, que funde hipóteses de superfícies planares e descarta detecções com pouco suporte, e uma etapa de otimização contínua, que refina as poses dos planos. Experiências com pares estéreo de ambientes interiores e exteriores confirmam melhorias significativas sobre métodos state-of-the-art relativamente a precisão e robustez. Finalmente, e como terceira contribuição para melhorar a visão estéreo na presença de superfícies inclinadas, estendemos o recente framework de agregação estéreo baseada em histogramas [4]. O algoritmo original utiliza janelas de suporte fronto-paralelas para a agregação de custo, o que leva a resultados imprecisos na presença de superfícies com inclinação significativa. Nós abordamos o problema considerando hipóteses de orientação discretas. Os resultados experimentais obtidos comprovam a eficácia do método, permitindo melhorar a precisção de correspondência, preservando simultaneamente uma baixa complexidade computacional.Recovering the 3D geometry from two or more views, known as stereo reconstruction, is one of the earliest and most investigated topics in computer vision. The computation of 3D models of an environment is useful for a very large number of applications, ranging from robotics, consumer utilization to medical procedures. The principle to recover the 3D scene structure is quite simple, however, there are some issues that considerable complicate the reconstruction process. Objects containing complicated structures, including low and repetitive textures, and highly slanted surfaces still pose difficulties to state-of-the-art algorithms. This PhD thesis tackles this issues and introduces a new stereo framework that is completely different from conventional approaches. We propose to use symmetry instead of photo-similarity for assessing the likelihood of two image locations being a match. The framework is called SymStereo, and is based on the mirroring effect that arises whenever one view is mapped into the other using the homography induced by a virtual cut plane that intersects the baseline. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo-Rangefinding that consists in using passive stereo to exclusively recover depth along a scan plane. Thorough experiments provide evidence that Stereo from Induced Symmetry is specially well suited for this purpose. As a second research line, we propose to overcome the previous issues using priors about the 3D scene for increasing the robustness of the reconstruction process. For this purpose, we present a new global approach for detecting vanishing points and groups of mutually orthogonal vanishing directions in man-made environments. Experiments in both synthetic and real images show that our algorithms outperform the state-of-the-art methods while keeping computation tractable. In addition, we show for the first time results in simultaneously detecting multiple Manhattan-world configurations. This prior information about the scene structure is then included in a reconstruction pipeline that generates piece-wise planar models of man-made environments from two calibrated views. Our formulation combines SymStereo and PEARL clustering [3], and alternates between a discrete optimization step, that merges planar surface hypotheses and discards detections with poor support, and a continuous optimization step, that refines the plane poses. Experiments with both indoor and outdoor stereo pairs show significant improvements over state-of-the-art methods with respect to accuracy and robustness. Finally, and as a third contribution to improve stereo matching in the presence of surface slant, we extend the recent framework of Histogram Aggregation [4]. The original algorithm uses a fronto-parallel support window for cost aggregation, leading to inaccurate results in the presence of significant surface slant. We address the problem by considering discrete orientation hypotheses. The experimental results prove the effectiveness of the approach, which enables to improve the matching accuracy while preserving a low computational complexity

Robust inversion and detection techniques for improved imaging performance

Thesis (Ph.D.)--Boston UniversityIn this thesis we aim to improve the performance of information extraction from imaging systems through three thrusts. First, we develop improved image formation methods for physics-based, complex-valued sensing problems. We propose a regularized inversion method that incorporates prior information about the underlying field into the inversion framework for ultrasound imaging. We use experimental ultrasound data to compute inversion results with the proposed formulation and compare it with conventional inversion techniques to show the robustness of the proposed technique to loss of data. Second, we propose methods that combine inversion and detection in a unified framework to improve imaging performance. This framework is applicable for cases where the underlying field is label-based such that each pixel of the underlying field can only assume values from a discrete, limited set. We consider this unified framework in the context of combinatorial optimization and propose graph-cut based methods that would result in label-based images, thereby eliminating the need for a separate detection step. Finally, we propose a robust method of object detection from microscopic nanoparticle images. In particular, we focus on a portable, low cost interferometric imaging platform and propose robust detection algorithms using tools from computer vision. We model the electromagnetic image formation process and use this model to create an enhanced detection technique. The effectiveness of the proposed technique is demonstrated using manually labeled ground-truth data. In addition, we extend these tools to develop a detection based autofocusing algorithm tailored for the high numerical aperture interferometric microscope