This dissertation, "3D Reconstruction and Camera Calibration From Circular-motion Image Sequences" by Yan, Li, 李燕, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "3D Reconstruction and Camera Calibration from Circular-Motion Image Sequences" Submitted by Li Yan for the degree of Doctor of Philosophy at The University of Hong Kong in December 2005 This thesis investigates the problem of 3D reconstruction from circular motion image sequences. The problem is normally resolved in two steps: projective reconstruction and then metric reconstruction by self-calibration. A key question considered in this thesis is how to make use of the circular motion information to improve the reconstruction accuracy and reduce the reconstruction ambiguity. The information is previously utilized by identifying the fixed image entities (e.g. the image of the rotation axis, vanishing line of the motion plane, etc). These fixed entities, however, only exist in constant intrinsic parameter sequences. In this thesis, circular motion constraints, which are valid for varying intrinsic parameter (e.g. zooming/refocusing) cameras, are formulated from the movement of camera center and principal plane. Based on the constraints, several novel algorithms are developed for each step of the whole 3D reconstruction procedure. For image sequences with known rotation angles, a circular projective reconstruction algorithm is proposed. We first formulate the circular motion constraints in the Euclidean frame, and then deduce the most general form of reconstruction in a projective frame that satisfies the circular motion constraints. The constraints are gradually enforced during an iterative process, resulting in a circular projective reconstruction. This approach can be used to deal with both cases of constant and varying intrinsic parameters. It is proved that the circular projective reconstruction retrieves metric reconstruction up to a two-parameter ambiguity representing a projective distortion along the rotation axis of the circular motion. Based on the circular projective reconstruction, a hierarchical self-calibration algorithm is proposed to estimate the remaining two parameters. Closed-form expressions of the absolute conic and its image are deduced in terms of the two parameters, which are then determined with zero-skew and unit aspect ratio assumptions. Alternatively, starting from a general (rather than circular) projective reconstruction, a stratified self-calibration algorithm is proposed to upgrade the projective reconstruction directly to a metric one. In this case, the plane at infinity is first identified with (i) the circular motion constraint on camera center and (ii) zero-skew and unit aspect ratio assumptions. With the knowledge of the plane at infinity, the camera calibration matrices can be readily obtained. All the above algorithms assume that the rotation angles are known. In the case if the angles are unknown, we provide two novel rotation angle recovery methods. For constant intrinsic parameter sequences, rotation angles can be recovered by using the fixed image entities. For varying intrinsic parameter sequences, it is shown that the movements of the camera center and principal plane form two concentric circles on the motion plane. By identifying the corresponding conic loci in 3D projective frame, the geometry of circular motion on the motion plane can be recovered. Compared with existing methods, the new method is more flexible in that it allows the intrinsic parameters to vary, and is simpler by avoi

This dissertation, "3D Metric Reconstruction From Uncalibrated Circular Motion Image Sequences" by Huang, Zhong, 鐘煌, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled 3D Metric Reconstruction from Uncalibrated Circular Motion Image Sequences submitted by Zhong Huang for the degree of Doctor of Philosophy at The University of Hong Kong May 2006 Circular motion is a practical motion for model acquisition. This thesis addresses the problem of metric reconstruction from uncalibrated circular motion image sequences from both the theoretical and practical viewpoints. The cameras are assumed to have constant intrinsic parameters, but the actual camera motion need not be known. A stratified geometric approach is taken to devise solutions. Algorithms for motion estimation, metric reconstruction and surface extraction are developed, by which an object model can be reconstructed systematically. First, a reconstruction method for circular motion is developed with the aim of estimating the unknown rotation angles of the camera robustly. This is achieved by enforcing the knowledge of the type of motion (i.e. the rotational motion constraint) in a factorization-based projective reconstruction, yielding what is called a circular projective reconstruction. Furthermore, a three-stage reconstruction approach with image points incrementally added in different stages of reconstructions is presented for computing a circular projective reconstruction from a long circular motion image sequence. This approach can cope with the missing data problem which inherently exists in a long image sequence. Second, the problem of computing a metric reconstruction from a circular projective reconstruction is considered. Two camera calibration methods are II proposed for resolving the metric reconstruction ambiguity in a circular projective reconstruction by exploring available calibration constraints. The methods are based on a new decomposition of a rectifying homography and developed for the cases involving one circular motion image sequence or one circular motion image sequence with one additional image. Finally, a fast model reconstruction method is proposed to extract the surface of an object from a calibrated circular motion image sequence. It is shown that 3D rim curves with known order enclosing the object can be reconstructed by means of the object silhouettes and feature points. These ordered rim curves allow a triangulated surface mesh of the object model to be constructed efficiently. Throughout the thesis, experimental results are given to demonstrate the performance of the proposed algorithms, and these results are shown to be both accurate and stable. III DOI: 10.5353/th_b3704379 Subjects: Three-dimensional imaging Image reconstruction Cameras - Calibration Algorithms

This title is part of a two volume set that constitutes the refereed proceedings of the 8th Asian Conference on Computer Vision, ACCV 2007. Coverage in this volume includes shape and texture, face and gesture, camera networks, face/gesture/action detection and recognition, learning, motion and tracking, human pose estimation, matching, face/gesture/action detection and recognition, low level vision and phtometory, motion and tracking, human detection, and segmentation.

"This book provides developers and scholars with an extensive collection of research articles in the expanding field of 3D reconstruction, investigating the concepts, methodologies, applications and recent developments in the field of 3D reconstruction"--

In this book, the design of two new planar patterns for camera calibration of intrinsic parameters is addressed and a line-based method for distortion correction is suggested. The dynamic calibration of structured light systems, which consist of a camera and a projector is also treated. Also, the 3D Euclidean reconstruction by using the image-to-world transformation is investigated. Lastly, linear calibration algorithms for the catadioptric camera are considered, and the homographic matrix and fundamental matrix are extensively studied. In these methods, analytic solutions are provided for the computational efficiency and redundancy in the data can be easily incorporated to improve reliability of the estimations. This volume will therefore prove valuable and practical tool for researchers and practioners working in image processing and computer vision and related subjects.