Shape from periodic texture using the eigenvectors of local affine distortion

This paper shows how the local slant and tilt angles of regularly textured curved surfaces can be estimated directly, without the need for iterative numerical optimization, We work in the frequency domain and measure texture distortion using the affine distortion of the pattern of spectral peaks. The key theoretical contribution is to show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local slant and tilt angles of tangent planes to curved surfaces. In particular, the leading eigenvector points in the tilt direction. Although not as geometrically transparent, the direction of the second eigenvector can be used to estimate the slant direction. The required affine distortion matrices are computed using the correspondences between spectral peaks, established on the basis of their energy ordering. We apply the method to a variety of real-world and synthetic imagery

Content based image pose manipulation

This thesis proposes the application of space-frequency transformations to the domain of pose estimation in images. This idea is explored using the Wavelet Transform with illustrative applications in pose estimation for face images, and images of planar scenes. The approach is based on examining the spatial frequency components in an image, to allow the inherent scene symmetry balance to be recovered. For face images with restricted pose variation (looking left or right), an algorithm is proposed to maximise this symmetry in order to transform the image into a fronto-parallel pose. This scheme is further employed to identify the optimal frontal facial pose from a video sequence to automate facial capture processes. These features are an important pre-requisite in facial recognition and expression classification systems. The under lying principles of this spatial-frequency approach are examined with respect to images with planar scenes. Using the Continuous Wavelet Transform, full perspective planar transformations are estimated within a featureless framework. Restoring central symmetry to the wavelet transformed images in an iterative optimisation scheme removes this perspective pose. This advances upon existing spatial approaches that require segmentation and feature matching, and frequency only techniques that are limited to affine transformation recovery. To evaluate the proposed techniques, the pose of a database of subjects portraying varying yaw orientations is estimated and the accuracy is measured against the captured ground truth information. Additionally, full perspective homographies for synthesised and imaged textured planes are estimated. Experimental results are presented for both situations that compare favourably with existing techniques in the literature

Video-Based In Situ Tagging on Mobile Phones

We propose a novel way to augment a real-world scene with minimal user intervention on a mobile phone; the user only has to point the phone camera to the desired location of the augmentation. Our method is valid for horizontal or vertical surfaces only, but this is not a restriction in practice in manmade environments, and it avoids going through any reconstruction of the 3-D scene, which is still a delicate process on a resource-limited system like a mobile phone. Our approach is inspired by recent work on perspective patch recognition, but we adapt it for better performances on mobile phones. We reduce user interaction with real scenes by exploiting the phone accelerometers to relax the need for fronto-parallel views. As a result, we can learn a planar target in situ from arbitrary viewpoints and augment it with virtual objects in real-time on a mobile phone

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

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

Local, Semi-Local and Global Models for Texture, Object and Scene Recognition

This dissertation addresses the problems of recognizing textures, objects, and scenes in photographs. We present approaches to these recognition tasks that combine salient local image features with spatial relations and effective discriminative learning techniques. First, we introduce a bag of features image model for recognizing textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. We present results of a large-scale comparative evaluation indicating that bags of features can be effective not only for texture, but also for object categization, even in the presence of substantial clutter and intra-class variation. We also show how to augment the purely local image representation with statistical co-occurrence relations between pairs of nearby features, and develop a learning and classification framework for the task of classifying individual features in a multi-texture image. Next, we present a more structured alternative to bags of features for object recognition, namely, an image representation based on semi-local parts, or groups of features characterized by stable appearance and geometric layout. Semi-local parts are automatically learned from small sets of unsegmented, cluttered images. Finally, we present a global method for recognizing scene categories that works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting spatial pyramid representation demonstrates significantly improved performance on challenging scene categorization tasks

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

A system for image-based modeling and photo editing

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2002.Includes bibliographical references (p. 169-178).Traditionally in computer graphics, a scene is represented by geometric primitives composed of various materials and a collection of lights. Recently, techniques for modeling and rendering scenes from a set of pre-acquired images have emerged as an alternative approach, known as image-based modeling and rendering. Much of the research in this field has focused on reconstructing and rerendering from a set of photographs, while little work has been done to address the problem of editing and modifying these scenes. On the other hand, photo-editing systems, such as Adobe Photoshop, provide a powerful, intuitive, and practical means to edit images. However, these systems are limited by their two-dimensional nature. In this thesis, we present a system that extends photo editing to 3D. Starting from a single input image, the system enables the user to reconstruct a 3D representation of the captured scene, and edit it with the ease and versatility of 2D photo editing. The scene is represented as layers of images with depth, where each layer is an image that encodes both color and depth. A suite of user-assisted tools are employed, based on a painting metaphor, to extract layers and assign depths. The system enables editing from different viewpoints, extracting and grouping of image-based objects, and modifying the shape, color, and illumination of these objects. As part of the system, we introduce three powerful new editing tools. These include two new clone brushing tools: the non-distorted clone brush and the structure-preserving clone brush. They permit copying of parts of an image to another via a brush interface, but alleviate distortions due to perspective foreshortening and object geometry.(cont.) The non-distorted clone brush works on arbitrary 3D geometry, while the structure-preserving clone brush, a 2D version, assumes a planar surface, but has the added advantage of working directly in 2D photo-editing systems that lack depth information. The third tool, a texture-illuminance decoupling filter, discounts the effect of illumination on uniformly textured areas by decoupling large- and small-scale features via bilateral filtering. This tool is crucial for relighting and changing the materials of the scene. There are many applications for such a system, for example architectural, lighting and landscape design, entertainment and special effects, games, and virtual TV sets. The system allows the user to superimpose scaled architectural models into real environments, or to quickly paint a desired lighting scheme of an interior, while being able to navigate within the scene for a fully immersive 3D experience. We present examples and results of complex architectural scenes, 360-degree panoramas, and even paintings, where the user can change viewpoints, edit the geometry and materials, and relight the environment.by Byong Mok Oh.Ph.D

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

THE ROLE OF TEXTURE IN INDOOR SCENE RECOGNITION

Ph.DDOCTOR OF PHILOSOPH