| This thesis discusses the problems of the recovery of a realistic textured model and the critical issues in metrology of 3D objects from uncalibrated image sequences. Traditional approaches are based on a preliminary calibration of the camera setup. This, however, is not always possible or practical. The goal of this work is to investigate the theoretical and practical feasibility in metrology of 3D objects from image sequences without any prior knowledge either about the parameters of the cameras, or about their motions. Meanwhile, several algorithms, such as how to improve the speed and the accuracy of reconstructed 3D model, are considered in this thesis. Main contributions are as follows:1. The bicubic B-spline smoothing filter operator is introduced. At the same time, a method for detecting the edge by using this operator is presented. The experimental results show that the method is efficient in edge detection, and it is very robust to noises.2. Two linear algorithms for estimating fundamental matrix are presented.i) Weighted normalizing algorithm. Weighted normalizing original input matching points with a weight factor related to residual errors and calculated the eight parameters of fundamental matrix by using the 8-point algorithm. The experimental results show that the algorithm is very robust to noises and outliers, and the fundamental matrix with high accuracy can be found.ii) Weighted translation algorithm. Normalized original input matching points and calculated the centroid coordinates by exploiting the strategy of weighted translation transformation, and the origins of coordinates are translated to their centroids. The eight parameters of fundamental matrix can be solved by using the 8-point algorithm and the procedure of estimating F-matrix with high accuracy can be achieved.3. A new method, the biepipole constraint algorithm, is developed to estimate the fundamental matrix (F-matrix) based on an 8-parameter model and the geometrical analysis. First, through the analysis of the new constraints, the four parameters of the F-matrix can be estimated by solving a nonlinear unconstraint optimization problem. The objective function of the optimization problem is an equation of degree six in four unknowns. Then, the four other parameters of the F-matrix can be evaluated by using the SVD method. Particular novelties of the algorithm are the obvious geometrical meanings of the parameters, fewer matching point pairs and higher accuracy.4. A new self-calibration method is presented. A set of equations is got by using the characteristic of 3-point perspective projective, vanish points and the orthogonal vector and then all the intrinsic parameters ax,ay,u0,vQ can be solved with high accuracy. This method also provides a credible initial value for nonlinear self-calibration method.In self-calibration, the prior knowledge of orthogonal/parallel lines and orthogonal walls are formulated as constraints on absolute quadric. The ambiguity on absolute quadric is reduced much more. Thus, the projective reconstruction and Euclidean reconstruction are more accurate and more realistic.5. A novel and efficient image rectification method using the fundamental matrix is proposed. In this approach, camera calibration is not required, and image resampling becomes very simple by using the Bresenham algorithm to extract pixels along the corresponding epipolar line. The rectified images are guaranteed to be effective for all possible camera motions, large or small. The loss of pixel information along the epipolar lines is minimized, and the size of rectified image is much smaller. Furthermore, it never splits the image and the connected regions will stay connected, even if the epipole locates inside an image. The effectiveness of the method is verified by an extensive set of real experiments. It shows that much more accurate matches of feature points can be obtained for a pair of images after the proposed rectification.6. A novel metrology without using any contacted tool is pr...
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