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

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 101-111). Also available in electronic version.

This dissertation investigates the feasibility of creating real-time three-dimensional images, using data obtained from satellites. The aim is to enhance satellite imaging applications, by utilizing the normal 3-D visual perceptions of humans. A study is made of the different methods developed to create the illusion of seeing a three-dimensional object from essentially two-dimensional images. 3-D display devices based on the principles of human stereoscopic vision do exist. Other 3-D display techniques include holograms and volumetric displays. Satellite images are used in a wide range of applications, from urban planning, to earth surveillance, and even weather prediction. In the past, satellite imaging was the express domain of experts, trained in the analysis and interpretation of satellite images. However, in recent years, the acquisition and analysis of satellite images have been greatly facilitated by the growing number of commercial satellites in our skies, as well as readily available software packages. Satellite images are available in many types of image formats, and can represent a large variety of information about an area. The model developed for this dissertation (the ACSI-3D model) proposes a method for the conversion of satellite images to suitable input for a stereoscopic 3-D display device. The model covers the process from initial image acquisition to the final display. It consists of four basic phases: Image Acquisition, Stereopsis, Sequencing and Synchronization, and Display. The “Stereoscopic Image Pair Creator” prototype was developed to test parts of this model. / Ehlers, E.M., Prof.

More and more computer applications are using three-dimensional models for a variety of uses (e.g. CAD, graphics, recognition). A major bottleneck is the acquisition of these models. The easiest method for designing the models is to build them directly from images of the object being modelled. This paper describes the design of a system, MOLASYS (for MOdeL Acquisition SYStem), that allows the user to build object models interactively from underlying images. This would not only be easier for the user, but also more accurate as the models will be built directly satisfying the dimensions, shape, and other constraints present in the images. The object models are constructed by constraining model points and edges to match points in the image objects. The constraints are defined by the user and expressed using a Jacobian matrix of partial derivatives of the errors with respect to a set of camera and model parameters. MOLASYS then uses Newton's method to solve for corrections to the parameters that will reduce the errors specified in the constraints to zero. Thus the user describes how the system will change, and the program determines the best way to accomplish the desired changes. The above techniques, implemented in MOLASYS, have resulted in an intuitive and flexible tool for the interactive creation of three-dimensional models. / Science, Faculty of / Computer Science, Department of / Graduate

No description available.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Acquiring 3-D motion of a camera from image sequences is one of the key components in a wide range of applications such as human computer interaction. Given the 3-D structure, the problem of camera motion recovery can he solved using the model-based approaches, which are well-known and have good performance under a controlled environment. If prior information on the scene is not available, traditional Structure from Motion (SFM) algorithms, which simultaneously estimate the scene structure and pose information, are required. The research presented in this thesis belongs to a different category: Motion from Motion (MFM), in which the main concern is the camera position and orientation. To be more precise, MFM algorithms have the capability of estimating 3-D camera motion directly from 2-D image motion without the explicit reconstruction of the scene structure, even though the 3-D model structure is not known in prior. As keeping track of the structural information is no longer required, putting these types of algorithms into real applications is relatively easy and convenient. / It is demonstrated in the experiments that the proposed algorithms are efficient, stable and accurate compared to several existing approaches. Furthermore, they have been put into applications such as mixed reality, virtual reality, robotics and super-resolution to show their performance in real situations. / The objective of this thesis is to develop a high-speed recursive approach that tackles the MFM problem. On the way to the final goal, a series of methods, each having its own strengths and characteristics, have been studied. (1) The first algorithm computes the camera pose from a monocular image sequence. The trifocal tensor is incorporated into the Extended Kalman Filter (EKF) formulation. The step of computing the 3-D models can thus be eliminated. (2) The proposed approach is then extended to the recovery of motion from a stereo image sequence. By applying the trifocal tensor to a stereo vision framework, the trifocal constraint becomes more robust and is not likely to be degenerate. In addition, the twist motion model is adopted to parameterize the 3-D motion. It does not suffer from singularities as Euler angles, and is minimal as opposed to quaternion and the direct use of rotation matrix. (3) The third method introduces the Interacting Multiple Model Probabilistic Data Association Filter (IMMPDAF) to the MFM problem. The Interacting Multiple Model (IMM) technique allows the existence of more than one dynamic system and in return leads to improved accuracy and stability even under abrupt motion changes. The Probabilistic Data Association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. As the PDA associates stereo correspondences probabilistically, the explicit establishment of stereo matches is not necessary except during initialization, and the point features present in the outer region of the stereo images can be utilized. / Yu, Ying Kin. / "July 2007." / Adviser: Wong Kin Hong. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1125. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 120-130). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

by Andrew Poon Ngai Ho. / Thesis (M.S.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [97-98]). / ABSTRACT / ACKNOWLEDGMENTS / Chapter 1. --- INTRODUCTION / Chapter 2. --- REPRESENTATION OP SPEECH DATA --- p.4 / Chapter 2.1 --- SAMPLE DATA REPRESENTATION --- p.4 / Chapter 2.2 --- ANALOG LINEAR SYSTEM MODEL --- p.7 / Chapter 2.3 --- DISCRETE FOURIER TRANSFORM --- p.8 / Chapter 2.4 --- FILTER BAND REPRESENTATION --- p.8 / Chapter 2.5 --- LINEAR PREDICTIVE CODING (LPC) --- p.10 / Chapter 2.2 --- LPC CEPSTRAL COEFFICIENT --- p.13 / Chapter 3. --- MULTI-DIMENSIONAL ANALYSIS --- p.18 / Chapter 3.1 --- PURE GRAPHICAL TOOLS --- p.18 / Chapter 3.1.1 --- MULTI-HISTOGRAM --- p.18 / Chapter 3.1.2 --- STARS --- p.19 / Chapter 3.1.3 --- SPIKED SCATTERPLOT --- p.19 / Chapter 3.1.4 --- GLYPHS --- p.22 / Chapter 3.1.5 --- BOXES --- p.22 / Chapter 3.1.6 --- LIMITATIONS OF THE BASIC METHODS --- p.22 / Chapter 3.1.7 --- CHERNOFF FACES --- p.26 / Chapter 3.1.8 --- ANDREW'S CURVE --- p.27 / Chapter 3.1.9 --- LIMITATIONS OF CHERNOFF FACES AND ANDREW'S CURVE --- p.30 / Chapter 3.1.10 --- SCATTERED PLOT MATRIX --- p.30 / Chapter 3.1.11 --- PARALLEL-AXIS SYSTEM --- p.32 / Chapter 3.1.12 --- COMMON BASIC PITFALL --- p.33 / Chapter 3.2 --- PURE PROJECTION METHODS --- p.36 / Chapter 3.2.1 --- PRINCIPAL COMPONENTS ANALYSIS --- p.36 / Chapter 3.2.2 --- PRINCIPLE CO-ORDINATES ANALYSIS --- p.37 / Chapter 3.2.3 --- REGRESSION ANALYSIS --- p.38 / Chapter 3.3 --- SLICED INVERSE REGRESSION (SIR) --- p.41 / Chapter 4 --- DATA ANALYSIS --- p.50 / Chapter 4.1 --- PROGRAMS AND TEST DATA --- p.50 / Chapter 4.2 --- ACTUAL SPEECH DATA RESULTS --- p.63 / Chapter 4.2.1 --- "SINGLE UTTERANCE OF ""4"" BY SPEAKER A ONLY" --- p.66 / Chapter 4.2.2 --- "TWELVE UTTERANCES OF ""4"" BY SPEAKER A" --- p.72 / Chapter 4.2.3 --- "THREE UTTERANCES PER SPEAKER OF ""4"" BY SPEAKER A, B AND C" --- p.78 / Chapter 4.2.4 --- "TWO UTTERANCES PER DIGIT OF ""1"" TO ""9"" BY SPEAKER A" --- p.83 / Chapter 4.2.5 --- "ONE UTTERANCE PER DIGIT PER SPEAKER OF ""1"" TO ""9"" BY SPEAKER A,B,C" --- p.86 / CONCLUSION AND FURTHER WORKS --- p.93 / Chapter 5.1 --- CONCLUSION --- p.93 / Chapter 5.2 --- FURTHER WORKS --- p.94 / REFERENCES / APPENDIX I MATLAB PROGRAM LISTING FOR SIR / APPENDIX 2 C PROGRAM LISTING FOR ROTATIONAL VIEW / APPENDIX 3 C PROGRAM LISTING FOR LPC AND CEPSTRAL TRANSFORMS / "APPENDIX 4 ALL VIEWS, EIGENVALUES AND EIGENVECTORS FOR SINGLE UTTERANCE OF ""4"" BY SPEAKER A" / "APPENDIX 5 ALL VIEWS, EIGENVALUES AND EIGENVECTORS FOR 12 UTTERANCES OF ""4"" BY SPEAKER A" / "APPENDIX 6 ALL VIEWS, EIGENVALUES AND EIGENVECTORS FOR 5 UTTERANCES PER SPEAKER OF ""4"" BY SPEAKER A,B,C" / "APPENDIX 7 ALL VIEWS, EIGENVALUES AND EIGENVECTORS FOR 2 UTTERANCES PER DIGIT OF DIGIT ""l"" TO ""9"" BY SPEAKER A" / "APPENDIX 8 ALL VIEWS, EIGENVALUES AND EIGENVECTORS FOR 1UTTERANCE PER SPEAKER PER DIGIT OF ""1"" TO ""9"" BY SPEAKER A,B,C"

by Tin Pong. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaf 73). / Abstract --- p.2 / Acknowledgement --- p.4 / Table of Contents --- p.5 / Chapter I. --- Introduction --- p.8 / Chapter II. --- Segmentation Tools --- p.11 / Chapter 2.1. --- Segmentation of Object --- p.11 / Chapter 2.1.1. --- Segmentation algorithm --- p.11 / Chapter 2.1.2. --- Region growing algorithm --- p.16 / Chapter 2.2. --- Noise Reduction --- p.19 / Chapter 2.2.1. --- Median filtering --- p.19 / Chapter 2.2.2 --- Mean filtering --- p.20 / Chapter 2.3. --- Other functions --- p.21 / Chapter 2.3.1. --- Contrast enhancement and reduction --- p.21 / Chapter 2.3.2. --- Brightness increment and reduction --- p.22 / Chapter III. --- 3D Visualization Tools --- p.23 / Chapter 3.1. --- Interpolation --- p.23 / Chapter 3.1.1. --- Estimate distance between slices --- p.23 / Chapter 3.1.2. --- Trilinear Interpolation --- p.24 / Chapter 3.2. --- Projection --- p.26 / Chapter 3.2.1. --- Parallel projection --- p.26 / Chapter 3.2.2. --- Z-Buffers --- p.27 / Chapter 3.3. --- Rotation of 3D image --- p.29 / Chapter 3.4. --- Shading --- p.30 / Chapter IV. --- Description of the software developed --- p.32 / Chapter 4.1. --- Programming environment --- p.32 / Chapter 4.2. --- Software developed --- p.32 / Chapter 4.3. --- 2D object segmentation panel --- p.35 / Chapter 4.4. --- 3D object segmentation panel --- p.45 / Chapter V. --- Results and analysis --- p.56 / Chapter 5.1. --- Results of segmentation of object --- p.56 / Chapter 5.2. --- Results of 3D visualization tools --- p.64 / Chapter VI. --- Future Development --- p.70 / Chapter VII. --- Conclusion --- p.72 / References --- p.73

Wai-kin Vong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 87-[91]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Three Dimensional Image --- p.1 / Chapter 1.2 --- Definition of segmentation --- p.2 / Chapter 1.3 --- 3D Image Segmentation --- p.3 / Chapter 1.4 --- Image Splitting Operation --- p.4 / Chapter 1.5 --- Region Merging Operation --- p.4 / Chapter 1.6 --- Split-and-merge Segmentation --- p.4 / Chapter 1.6.1 --- Selection of particular operators --- p.5 / Chapter 2 --- Overview of Image Segmentation Techniques --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- Edge Based Method --- p.6 / Chapter 2.2.1 --- 3D Laplacian of Gaussian Filtering --- p.7 / Chapter 2.2.2 --- 3D Deformable Surfaces [8] --- p.11 / Chapter 2.3 --- Region Based Method --- p.14 / Chapter 2.3.1 --- 3D oct-tree split-and-merge --- p.15 / Chapter 2.3.2 --- 3D pyramid segmentation --- p.17 / Chapter 2.4 --- 2D segmentation Approaches --- p.20 / Chapter 2.4.1 --- 2D Image segmentation by shape description --- p.20 / Chapter 2.4.2 --- Morphological Watershed Transform (WT) --- p.23 / Chapter 2.5 --- Discussion --- p.34 / Chapter 3 --- Modification Of Digital Watershed Transform (DWT) --- p.36 / Chapter 3.1 --- Introduction --- p.36 / Chapter 3.2 --- Edge Detection --- p.37 / Chapter 3.2.1 --- Discrete Non-linear Edge Detectors --- p.37 / Chapter 3.2.2 --- Canny's Edge Detector --- p.40 / Chapter 3.2.3 --- Gradient of Gaussian Filter --- p.42 / Chapter 3.3 --- Digital Watershed Transform --- p.46 / Chapter 3.3.1 --- Introduction --- p.46 / Chapter 3.3.2 --- Modification of SKIZ --- p.46 / Chapter 3.3.3 --- Implementation --- p.51 / Chapter 4 --- Region Modeling --- p.55 / Chapter 4.1 --- Introduction --- p.55 / Chapter 4.2 --- Texture Definition --- p.57 / Chapter 4.3 --- Texture Modeling --- p.58 / Chapter 4.3.1 --- Markov Random Field (MRF) --- p.58 / Chapter 4.3.2 --- Simultaneous Autoregressive (SAR) Model --- p.59 / Chapter 4.3.3 --- Parameter Estimation --- p.61 / Chapter 4.3.4 --- A Simple model --- p.63 / Chapter 4.3.5 --- Combination of MRF parameters --- p.63 / Chapter 4.3.6 --- Similarity Measure --- p.66 / Chapter 4.4 --- Model Evaluation --- p.68 / Chapter 4.4.1 --- Classification of Different Materials --- p.68 / Chapter 4.4.2 --- Rotational Invariance --- p.69 / Chapter 4.5 --- Results and Observations --- p.72 / Chapter 5 --- Three-Dimensional Segmentation with Interactive Labeling --- p.73 / Chapter 5.1 --- Introduction --- p.73 / Chapter 5.2 --- Region Merging Scheme --- p.75 / Chapter 5.3 --- Interactive Labeling --- p.76 / Chapter 5.4 --- Experiment of 3D Guided Segmentation --- p.77 / Chapter 6 --- Conclusion --- p.81 / Chapter 6.1 --- Image Partitioning by Watershed Transform --- p.81 / Chapter 6.2 --- Image modeling by Markov Random Field --- p.82 / Chapter 6.3 --- 3D image segmentation --- p.82 / A --- p.84 / B --- p.86 / Bibliography --- p.87