Thesis (M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (ℓ. 50-51)

Thesis (M.S. in Physics)--Naval Postgraduate School, December 1990. / Thesis Advisor(s): Walters, Donald L. ; Matson, Charles L. ; Roggeman, Michael C. Second Reader: Davis, David S. "December 1990." Description based on title screen as viewed on March 31, 2010. DTIC Identifier(s): Knox-Thompson Techniques, Triple Correlation. Author(s) subject terms: Image Reconstruction, Knox-Thompson, Triple-Correlation. Includes bibliographical references (p. 49-50). Also available in print.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Vita. Includes bibliographical references (leaves 119-133). Also available in electronic version.

No description available.

Statistical methods for approaching image reconstruction and restoration problems have generated much interest among statisticians in the past decade. In this dissertation, we examine in detail various statistical methods of image reconstruction through the simulation of a multiple-pinhole coded-aperture imaging system for use in emission tomography. We reconstruct each object from a class of 64 total objects, obtaining a reconstruction for each of the 64 originals by several different methods. Among the methods that we use to obtain these reconstructions are maximum likelihood techniques, where we make use of both the popular expectation-maximization (EM) algorithm and a Monte Carlo search routine. We also examine methods that include, in some form, various kinds of prior information. One way of using prior information is through the specification of a prior probability density on the object (or class of objects) to be reconstructed. We investigate the use of Markov random field (MRF) models as a means of specifying the prior densities that will be used to obtain reconstructions. Once given a prior density, these reconstructions are taken to be approximations to the maximum a posteriori (MAP) estimate of the original object. We also investigate reconstructions obtained through other prior densities plus reconstructions obtained by introducing prior information in alternate ways. Once all the reconstructions are obtained, we attempt to answer the important question, "which reconstruction method is 'best'?" We define "best" in this context to be the method that allows a human observer to perform a specified task the most accurately. The task to be performed is to determine whether or not a small protrusion exists on an elliptical object. (This task is motivated by the desire to detect wall-motion abnormalities in the left ventricle of the heart.) We generate 32 objects with protrusions (abnormal objects) and 32 objects without protrusions (normal objects). These objects constitute our class of 64 originals which are reconstructed by the various methods. The reconstruction methods are then analyzed through receiver operating characteristic (ROC) analysis, and a performance index, the area under the curve (AUC), is obtained for each method. Statistical tests are then performed on certain pairs of methods so that the hypothesis that no difference between the AUC's exists can be tested. We found that the reconstruction methods that used the largest amount of (accurate) prior information were generally superior to other methods considered. We also compute calculable figures of merit (FOM) associated with each reconstruction method with the hope that these FOM's will predict the performance of the human observer. Unfortunately, our results indicate that the FOM's that we considered do not correlate well with the performance of the human.

No description available.

No description available.

Lau Tak Fu. / Thesis submitted in: October 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 93-98). / Abstracts in English and Chinese. / Chapter 1 - --- Introduction --- p.9 / Chapter 1.1 --- Scope of the work --- p.9 / Chapter 1.2 --- Main contribution --- p.11 / Chapter 1.3 --- Outline of the thesis --- p.12 / Chapter 2 - --- Background --- p.14 / Chapter 2.1 --- Three dimensional models from images --- p.14 / Chapter 2.2 --- Un-calibrated 3D reconstruction --- p.14 / Chapter 2.3 --- Self calibrated 3D reconstruction --- p.16 / Chapter 2.4 --- Initial model formation using image based --- p.18 / Chapter 2.5 --- Volumes from Silhouettes --- p.19 / Chapter 3 - --- Initial model reconstruct the problem with mismatch noise --- p.22 / Chapter 3.1 --- Perspective Camera Model --- p.24 / Chapter 3.2 --- "Intrinsic parameters, Extrinsic parameters and camera motion" --- p.25 / Chapter 3.2.1 --- Intrinsic parameters --- p.25 / Chapter 3.2.2 --- Extrinsic parameter and camera motion --- p.27 / Chapter 3.3 --- Lowe's method --- p.29 / Chapter 3.4 --- Interleave bundle adjustment for structure and motion recovery from multiple images --- p.32 / Chapter 3.5 --- Feature points mismatch analysis --- p.38 / Chapter 4 - --- Feature selection by using look forward silhouette clipping --- p.43 / Chapter 4.1 --- Introduction to silhouette clipping --- p.43 / Chapter 4.2 --- Silhouette clipping for 3D model --- p.45 / Chapter 4.3 --- Implementation --- p.52 / Chapter 4.3.1 --- Silhouette extraction program --- p.52 / Chapter 4.3.2 --- Feature filter for alternative bundle adjustment algorithm --- p.59 / Chapter 5 - --- Experimental data --- p.61 / Chapter 5.1 --- Simulation --- p.61 / Chapter 5.1.1 --- Input of simulation --- p.61 / Chapter 5.1.2 --- Output of the simulation --- p.66 / Chapter 5.1.2.1 --- Radius distribution --- p.66 / Chapter 5.1.2.2 --- 3D model output --- p.74 / Chapter 5.1.2.3 --- VRML plotting --- p.80 / Chapter 5.2 --- Real Image testing --- p.82 / Chapter 5.2.1 --- Toy house on a turntable test --- p.82 / Chapter 5.2.2 --- Other tests on turntable --- p.86 / Chapter 6 - --- Conclusion and discussion --- p.89

Ip Che Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 119-127). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Previous Work --- p.2 / Chapter 1.2.1 --- Reconstruction for Architecture Scene --- p.2 / Chapter 1.2.2 --- Super-resolution --- p.4 / Chapter 1.2.3 --- Coarse-to-Fine Approach --- p.4 / Chapter 1.3 --- Proposed solution --- p.6 / Chapter 1.4 --- Contribution --- p.6 / Chapter 1.5 --- Publications --- p.7 / Chapter 1.6 --- Layout of the thesis --- p.7 / Chapter 2 --- Background Techniques --- p.8 / Chapter 2.1 --- Interest Point Detectors --- p.8 / Chapter 2.1.1 --- Scale-space --- p.9 / Chapter 2.1.2 --- Harris Corner detectors --- p.10 / Chapter 2.1.3 --- Other Kinds of Interest Point Detectors --- p.17 / Chapter 2.1.4 --- Summary --- p.18 / Chapter 2.2 --- Steerable filters --- p.19 / Chapter 2.2.1 --- Orientation estimation --- p.20 / Chapter 2.3 --- Point Descriptors --- p.22 / Chapter 2.3.1 --- Image derivatives under illumination change --- p.23 / Chapter 2.3.2 --- Image derivatives under geometric scale change --- p.24 / Chapter 2.3.3 --- An example of a point descriptor --- p.25 / Chapter 2.3.4 --- Other examples --- p.25 / Chapter 2.4 --- Feature Tracking Techniques --- p.26 / Chapter 2.4.1 --- Kanade-Lucas-Tomasi (KLT) Tracker --- p.26 / Chapter 2.4.2 --- Guided Tracking Algorithm --- p.28 / Chapter 2.5 --- RANSAC --- p.29 / Chapter 2.6 --- Structure-from-motion (SFM) Algorithm --- p.31 / Chapter 2.6.1 --- Factorization methods --- p.33 / Chapter 2.6.2 --- Epipolar Geometry --- p.39 / Chapter 2.6.3 --- Bundle Adjustment --- p.47 / Chapter 2.6.4 --- Summary --- p.50 / Chapter 3 --- Hierarchical Registration of 3D Models --- p.52 / Chapter 3.1 --- Overview --- p.53 / Chapter 3.1.1 --- The Arrangement of Image Sequences --- p.53 / Chapter 3.1.2 --- The Framework --- p.54 / Chapter 3.2 3 --- D Model Reconstruction for Each Sequence --- p.57 / Chapter 3.3 --- Multi-scale Image Matching --- p.59 / Chapter 3.3.1 --- Scale-space interest point detection --- p.61 / Chapter 3.3.2 --- Point descriptor --- p.61 / Chapter 3.3.3 --- Point-to-point matching --- p.63 / Chapter 3.3.4 --- Image transformation estimation --- p.64 / Chapter 3.3.5 --- Multi-level image matching --- p.66 / Chapter 3.4 --- Linkage Establishment --- p.68 / Chapter 3.5 --- 3D Model Registration --- p.70 / Chapter 3.6 --- VRML Modelling --- p.73 / Chapter 4 --- Experiment --- p.74 / Chapter 4.1 --- Synthetic Experiments --- p.74 / Chapter 4.1.1 --- Study on Rematching Algorithm --- p.74 / Chapter 4.1.2 --- Comparison between Affine and Metric transforma- tions for 3D Registration --- p.80 / Chapter 4.2 --- Real Scene Experiments --- p.86 / Chapter 5 --- Conclusion --- p.112 / Chapter 5.1 --- Future Work --- p.114 / Chapter A --- Camera Parameters --- p.116 / Chapter A.1 --- Intrinsic Parameters --- p.116 / Chapter A.2 --- Extrinsic Parameters --- p.117 / Bibliography --- p.127

Thesis (Ph. D.)--University of Chicago, Department of Radiology, June 2001. / Includes bibliographical references. Also available on the Internet.