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

Computer vision is concerned with the recovery of useful scene or camera information from a set of images. One classical problem is the estimation of the 3D scene structure depicted in multiple photographs. Such estimation fundamentally requires determining how the cameras are related in space. For a dynamic event recorded by multiple video cameras, finding the temporal relationship between cameras has a similar importance. Estimating such synchrony is key to a further analysis of the dynamic scene components. Existing approaches to synchronisation involve using visual cues common to both videos, and consider a discrete uniform range of synchronisation hypotheses. These prior methods exploit known constraints which hold in the presence of synchrony, from which both a temporal relationship, and an unchanging spatial relationship between the cameras can be recovered. This thesis presents methods that synchronise a pair of independently moving cameras. The spatial configuration of cameras is assumed to be known, and a cost function is developed to measure the quality of synchrony even for accuracies within a fraction of a frame. A Histogram method is developed which changes the approach from a consideration of multiple synchronisation hypotheses, to searching for seemingly synchronous frame pairs independently. Such a strategy has increased efficiency in the case of unknown frame rates. Further savings can be achieved by reducing the sampling rate of the search, by only testing for synchrony across a small subset of frames. Two robust algorithms are devised, using Bayesian inference to adaptively seek the sampling rate that minimises total execution time. These algorithms have a general underlying premise, and should be applicable to a wider class of robust estimation problems. A method is also devised to robustly synchronise two moving cameras when their spatial relationship is unknown. It is assumed that the motion of each camera has been estimated independently, so that these motion estimates are unregistered. The algorithm recovers both a synchronisation estimate, and a 3D transformation that spatially registers the two cameras. / Thesis (Ph.D.) - University of Adelaide, School of Computer Science, 2008

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 48-51). Also available on the World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 116-123).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vii, 124 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 76).

Thesis (M.S.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (February 23, 2006) Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

The ability to create large-scale high-resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function. This is particularly important for brain tissue, where the majority of function occurs at the cellular and sub-cellular level. However, reconstructing tissue at sub-cellular resolution is a complex problem that requires new methods for imaging and data analysis. In this dissertation, I describe a prototype microscopy technique that can image large volumes of tissue at sub-cellular resolution. This method, known as Knife-Edge Scanning Microscopy (KESM), has an extremely high data rate and can capture large tissue samples in a reasonable time frame. We can therefore image complete systems of cells, such as whole small animal organs, in a matter of days. I then describe algorithms that I have developed to cope with large and complex data sets. These include methods for improving image quality, tracing filament networks, and constructing high-resolution anatomical models. These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high-throughput microscopy data. The resulting models of large-scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques.