To make three dimensional measurements using a stereo camera system, the intrinsic and extrinsic calibration of the system should be obtained. Furthermore, to allow zooming, intrinsic parameters should be re-estimated using only scene constraints. In this study both manual and autocalibration algorithms are implemented and tested. The implemented manual calibration system is used to calculate the parameters of the calibration with the help of a planar calibration object. The method is tested on different internal calibration settings and results of 3D measurements using the obtained calibration is presented. Two autocalibration methods have been implemented. The first one requires a general motion while the second method requires a pure rotation of the cameras. The autocalibration methods require point matches between images. To achieve a fully automated process, robust algorithms for point matching have been implemented. For the case of general motion the fundamental matrix relation is used in the matching algorithm. When there is only rotation between views, the homography relation is used. The results of variations on the autocalibration methods are also presented. The result of the manual calibration has been found to be very reliable. The results of the first autocalibration method are not accurate enough but it has been shown that the calibration from rotating cameras performs precise enough if rotation between images is sufficiently large.

Perception of depth information is central to three-dimensional (3D) vision problems. Stereopsis is an important passive vision technique for depth perception. Wide baseline stereo is a challenging problem that attracts much interest recently from both the theoretical and application perspectives. In this research we approach the problem of wide baseline stereo using the geometric and structural constraints within feature sets. The major contribution of this dissertation is that we proposed and implemented a more efficient paradigm to handle the challenges introduced by perspective distortion in wide baseline stereo, compared to the state-of-the-art. To facilitate the paradigm, a new feature-matching algorithm that extends the state-of-the-art matching methods to larger baseline cases is proposed. The proposed matching algorithm takes advantage of both the local feature descriptor and the structure pattern of the feature set, and enhances the matching results in the case of large viewpoint change. In addition, an innovative rectification for uncalibrated images is proposed to make wide baseline stereo dense matching possible. We noticed that present rectification methods did not take into account the need for shape adjustment. By introducing the geometric constraints of the pattern of the feature points, we propose a rectification method that maximizes the structure congruency based on Delaunay triangulation nets and thus avoid some existing problems of other methods. The rectified stereo images can then be used to generate a dense depth map of the scene. The task is much simplified compared to some existing method because the 2D searching problem is reduced to 1D searching. To validate the proposed methods, real world images are applied to test the performance and comparisons to the state-of-the-art methods are provided. The performance of the dense matching with respect to the changing baseline is also studied.

在電腦硬體快速發展的時代，以前被視為只能在工作站上執行的3D繪圖現今也可以輕易地在普通的家用電腦上執行。藉著網路視訊攝影機低廉的價格以及其普及化的趨勢，我們設計一套兼顧快速以及經濟性的系統。使用者可以輕易自行架構攝影環境，不需專業的操作便可以自動產生立體的人臉誇張模型。另一方面我們改良並結合先前的2D誇張肖像漫畫研究，除了大幅提升五官定位的精確度外，藉著替換多張參考畫作，產生出來的模型可以呈現不同藝術風格的外觀，以供廣泛的娛樂用途。 / With the advances of hardware nowadays, the computation and demonstration of 3D graphics are readily attained on a personal computer. At the same time, thanks to the prevalence of instant messaging software, webcam has become very popular. These combined factors have motivated us to design an economic and effective solution for general users to create their own 3D caricature models without complex procedures or instructions. In this thesis, we have developed a system which can extract and analyze facial features from an image pair captured by two webcams, and then generate a 3D face model based on it. Specifically, we improved previous research to obtain more accurate feature locations, and extended the exaggeration algorithm to generate more impressive effects. By fusing a caricature produced with different work as references, the system is capable of painting the face model with various artists’ styles.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

abstract: The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, wet conditions etc... To allow the implementation of VO on any generic vehicle, a discussion on porting of the VO algorithm to android handsets is presented too. The SVO is implemented in three steps. In the first step, a dense disparity map for a scene is computed. To achieve this we utilize sum of absolute differences technique for stereo matching on rectified and pre-filtered stereo frames. Epipolar geometry is used to simplify the matching problem. The second step involves feature detection and temporal matching. Feature detection is carried out by Harris corner detector. These features are matched between two consecutive frames using the Lucas-Kanade feature tracker. The 3D co-ordinates of these matched set of features are computed from the disparity map obtained from the first step and are mapped into each other by a translation and a rotation. The rotation and translation is computed using least squares minimization with the aid of Singular Value Decomposition. Random Sample Consensus (RANSAC) is used for outlier detection. This comprises the third step. The accuracy of the algorithm is quantified based on the final position error, which is the difference between the final position computed by the SVO algorithm and the final ground truth position as obtained from the GPS. The SVO showed an error of around 1% under normal conditions for a path length of 60 m and around 3% in bright conditions for a path length of 130 m. The algorithm suffered in presence of shadows and vibrations, with errors of around 15% and path lengths of 20 m and 100 m respectively. / Dissertation/Thesis / M.S. Electrical Engineering 2010

Visual tracking concerns the problem of following an arbitrary object in a video sequence. In this thesis, we examine how to use stereo images to extend existing visual tracking algorithms, which methods exists to obtain information from stereo images, and how the results change as the parameters to each tracker vary. For this purpose, four abstract approaches are identified, with five distinct implementations. Each tracker implementation is an extension of a baseline algorithm, MOSSE. The free parameters of each model are optimized with respect to two different evaluation strategies called nor- and wir-tests, and four different objective functions, which are then fixed when comparing the models against each other. The results are created on single target tracks extracted from the KITTI tracking dataset, and the optimization results show that none of the objective functions are sensitive to the exposed parameters under the joint selection of model and dataset. The evaluation results also shows that none of the extensions improve the results of the baseline tracker.

Esta tese apresenta um novo método para interpolação de vistas em vídeos usando câmeras ao longo de um baseline baseado em uma triangulação 2D. A imagem de referência é primeiramente particionada em regiões triangulares usando informação de bordas e escala, visando colocar vértices ao longo das bordas da imagem e aumentar o número de triângulos em regiões texturadas. Um algoritmo de casamento de regiões é então usado para encontrar a disparidade inicial de cada triângulo, e uma etapa de refinamento é aplicada para mudar a disparidade nos vértices dos triângulos, gerando um mapa de disparidade linear em trechos. Uma simples etapa de pós-processamento é aplicada para conectar os triângulos com disparidade semelhante, gerando uma malha 3D relacionada a cada câmera, que são usadas para gerar novas vistas sintéticas ao longo do mesmo baseline das câmeras. Para gerar vistas com menos artefatos temporais (flickering), foi proposta uma abordagem para atualizar a malha 3D inicial dinamicamente, movendo, removendo e inserindo vértices a cada quadro baseado no fluxo óptico. Esta abordagem permite relacionar triângulos da malha ao longo do tempo, e uma combinação de Modelo Oculto de Markov, aplicado nos triângulos que persistem ao longo do tempo, com Filtro de Kalman, aplicado nos vértices, permite a geração de uma mapa de disparidade com coerência temporal. Com a abordagem proposta, o processo de gerar vistas interpoladas se reduz à trivial tarefa de renderizar uma malha poligonal, algo que pode ser feito muito rapidamente, principalmente quando placas gráficas são utilizadas. Além disso, as vistas geradas não possuem buracos, diferente de muitas técnicas de interpolação de vistas baseadas em pixels que requerem procedimentos de pós-processamento para preencher buracos. Os resultados experimentais indicam que a abordagem proposta foi capaz de gerar vistas interpoladas visualmente coerentes em vídeos desafiadores, com luz natural e movimento de câmera. Além disso, uma avaliação quantitativa usando métricas de qualidade de vídeos mostrou que as sequências de video interpoladas são melhores que abordagens competitivas. / This thesis presents a new method for video view interpolation using multiview linear camera arrays based on 2D domain triangulation. The domain of the reference image is initially partitioned into triangular regions using edge and scale information, aiming to place vertices along image edges and to increase the number of triangles in textured regions. A region-based matching algorithm is then used to find an initial disparity for each triangle, and a refinement stage is applied to change the disparity at the vertices of the triangles, generating a piecewise linear disparity map. A simple post-processing procedure is applied to connect the triangles with similar disparities, generating a full 3D mesh related to each camera (view), which are used to generate the new synthesized views along the cameras baseline. In order to generate views with less temporal flickering artifacts, we propose a scheme to update the initial 3D mesh dynamically, by moving, deleting and inserting vertices at each frame based on optical flow. This approach allows to relate triangles of the mesh across time, and a combination of Hidden Markov Models (HMMs), applied to time-persistent triangles, with the Kalman Filter, applied to vertices, so that temporal consistency can also be obtained. With the proposed framework, view interpolation reduces to the trivial task of rendering polygonal meshes, which can be done very fast, particularly when GPUs are employed. Furthermore, the generated views are hole-free, unlike most point-based view interpolation schemes that require some kind of post-processing procedures to fill holes. Experimental results indicate that our approach was able to generate visually coherent in-between interpolated views for challenging, real-world videos with natural lighting and camera movement.

In this thesis, a novel and fast method to compute the dense disparity map of a stereo pair of images is presented. Most of the current stereo matching algorithms are ill suited for real-time matching owing to their time complexity. Methods that concentrate on providing a real-time performance, sacrifice much in accuracy. The presented method, Fast Estimation of Dense Disparity Map Using Pivot Points (FEDDUP), uses a hierarchical approach towards reduction of search space to find the correspondences. The hierarchy starts with a set of points and then it moves on to a mesh with which the edge pixels are matched. This results in a semi-global disparity map. The semi global disparity map is then used as a soft constraint to find the correspondences of the remaining points. This process delivers good real-time performance with promising accuracy.

abstract: Blur is an important attribute in the study and modeling of the human visual system. In this work, 3D blur discrimination experiments are conducted to measure the just noticeable additional blur required to differentiate a target blur from the reference blur level. The past studies on blur discrimination have measured the sensitivity of the human visual system to blur using 2D test patterns. In this dissertation, subjective tests are performed to measure blur discrimination thresholds using stereoscopic 3D test patterns. The results of this study indicate that, in the symmetric stereo viewing case, binocular disparity does not affect the blur discrimination thresholds for the selected 3D test patterns. In the asymmetric viewing case, the blur discrimination thresholds decreased and the decrease in threshold values is found to be dominated by the eye observing the higher blur. The second part of the dissertation focuses on texture granularity in the context of 2D images. A texture granularity database referred to as GranTEX, consisting of textures with varying granularity levels is constructed. A subjective study is conducted to measure the perceived granularity level of textures present in the GranTEX database. An objective index that automatically measures the perceived granularity level of textures is also presented. It is shown that the proposed granularity metric correlates well with the subjective granularity scores and outperforms the other methods presented in the literature. A subjective study is conducted to assess the effect of compression on textures with varying degrees of granularity. A logarithmic function model is proposed as a fit to the subjective test data. It is demonstrated that the proposed model can be used for rate-distortion control by allowing the automatic selection of the needed compression ratio for a target visual quality. The proposed model can also be used for visual quality assessment by providing a measure of the visual quality for a target compression ratio. The effect of texture granularity on the quality of synthesized textures is studied. A subjective study is presented to assess the quality of synthesized textures with varying levels of texture granularity using different types of texture synthesis methods. This work also proposes a reduced-reference visual quality index referred to as delta texture granularity index for assessing the visual quality of synthesized textures. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

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