From pose estimation to structure and motion.

Yu Ying-Kin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 108-116).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation and Objectives --- p.1Chapter 1.2 --- Problem Definition --- p.3Chapter 1.3 --- Contributions --- p.6Chapter 1.4 --- Related Publications --- p.8Chapter 1.5 --- Organization of the Paper --- p.9Chapter 2 --- Background --- p.11Chapter 2.1 --- Introduction --- p.11Chapter 2.2 --- Pose Estimation --- p.12Chapter 2.2.1 --- Overview --- p.12Chapter 2.2.2 --- Lowe's Method --- p.14Chapter 2.2.3 --- The Genetic Algorithm by Hati and Sen- gupta --- p.15Chapter 2.3 --- Structure and Motion --- p.17Chapter 2.3.1 --- Overview --- p.17Chapter 2.3.2 --- The Extended Lowe's Method --- p.20Chapter 2.3.3 --- The Extended Kalman Filter by Azarbaye- jani and Pentland --- p.23Chapter 3 --- Model-based Pose Tracking Using Genetic Algo- rithms --- p.27Chapter 3.1 --- Introduction --- p.27Chapter 3.2 --- Overview of the Algorithm --- p.28Chapter 3.3 --- Chromosome Encoding --- p.29Chapter 3.4 --- The Genetic Operators --- p.30Chapter 3.4.1 --- Mutation --- p.30Chapter 3.4.2 --- Crossover --- p.31Chapter 3.5 --- Fitness Evaluation --- p.31Chapter 3.6 --- The Roulette Wheel Proportionate Selection Scheme --- p.32Chapter 3.7 --- The Genetic Algorithm Parameters --- p.33Chapter 3.8 --- Experiments and Results --- p.34Chapter 3.8.1 --- Synthetic Data Experiments --- p.34Chapter 3.8.2 --- Real Scene Experiments --- p.38Chapter 4 --- Recursive 3D Structure Acquisition Based on Kalman Filtering --- p.42Chapter 4.1 --- Introduction --- p.42Chapter 4.2 --- Overview of the Algorithm --- p.43Chapter 4.2.1 --- Feature Extraction and Tracking --- p.44Chapter 4.2.2 --- Model Initialization --- p.44Chapter 4.2.3 --- Structure and Pose Updating --- p.45Chapter 4.3 --- Structure Updating --- p.46Chapter 4.4 --- Pose Estimation --- p.49Chapter 4.5 --- Handling of the Changeable Set of Feature Points --- p.52Chapter 4.6 --- Analytical Comparisons with Other Algorithms --- p.54Chapter 4.6.1 --- Comparisons with the Interleaved Bundle Adjustment Method --- p.54Chapter 4.6.2 --- Comparisons with the EKF by Azarbaye- jani and Pentland --- p.56Chapter 4.7 --- Experiments and Results --- p.57Chapter 4.7.1 --- Synthetic Data Experiments --- p.57Chapter 4.7.2 --- Real Scene Experiments --- p.58Chapter 5 --- Simultaneous Pose Tracking and Structure Acqui- sition Using the Interacting Multiple Model --- p.63Chapter 5.1 --- Introduction --- p.63Chapter 5.2 --- Overview of the Algorithm --- p.65Chapter 5.2.1 --- Feature Extraction and Tracking --- p.65Chapter 5.2.2 --- Model Initialization --- p.66Chapter 5.2.3 --- Structure and Pose Updating --- p.66Chapter 5.3 --- Pose Estimation --- p.67Chapter 5.3.1 --- The Interacting Multiple Model Algorithm --- p.67Chapter 5.3.2 --- Design of the Individual EKFs --- p.71Chapter 5.4 --- Structure Updating --- p.74Chapter 5.5 --- Handling of the Changeable Set of Feature Points --- p.76Chapter 5.6 --- Analytical Comparisons with Other EKF-Based Algorithms --- p.77Chapter 5.6.1 --- Computation Speed --- p.77Chapter 5.6.2 --- Accuracy of the Recovered Pose Sequences --- p.79Chapter 5.7 --- Experiments and Results --- p.80Chapter 5.7.1 --- Synthetic Data Experiments --- p.80Chapter 5.7.2 --- Real Scene Experiments --- p.80Chapter 6 --- Empirical Comparisons of the Structure and Mo- tion Algorithms --- p.87Chapter 6.1 --- Introduction --- p.87Chapter 6.2 --- Comparisons Using Synthetic Data --- p.88Chapter 6.2.1 --- Image Residual Errors --- p.88Chapter 6.2.2 --- Computation Efficiency --- p.89Chapter 6.2.3 --- Accuracy of Recovered Pose Sequences . . --- p.91Chapter 6.3 --- Comparisons Using Real Images --- p.92Chapter 6.4 --- Summary --- p.97Chapter 7 --- Future Work --- p.99Chapter 8 --- Conclusion --- p.101Chapter A --- Kalman Filtering --- p.103Bibliography --- p.10

Applying image processing techniques to pose estimation and view synthesis.

Fung Yiu-fai Phineas.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 142-148).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Model-based Pose Estimation --- p.3Chapter 1.1.1 --- Application - 3D Motion Tracking --- p.4Chapter 1.2 --- Image-based View Synthesis --- p.4Chapter 1.3 --- Thesis Contribution --- p.7Chapter 1.4 --- Thesis Outline --- p.8Chapter 2 --- General Background --- p.9Chapter 2.1 --- Notations --- p.9Chapter 2.2 --- Camera Models --- p.10Chapter 2.2.1 --- Generic Camera Model --- p.10Chapter 2.2.2 --- Full-perspective Camera Model --- p.11Chapter 2.2.3 --- Affine Camera Model --- p.12Chapter 2.2.4 --- Weak-perspective Camera Model --- p.13Chapter 2.2.5 --- Paraperspective Camera Model --- p.14Chapter 2.3 --- Model-based Motion Analysis --- p.15Chapter 2.3.1 --- Point Correspondences --- p.16Chapter 2.3.2 --- Line Correspondences --- p.18Chapter 2.3.3 --- Angle Correspondences --- p.19Chapter 2.4 --- Panoramic Representation --- p.20Chapter 2.4.1 --- Static Mosaic --- p.21Chapter 2.4.2 --- Dynamic Mosaic --- p.22Chapter 2.4.3 --- Temporal Pyramid --- p.23Chapter 2.4.4 --- Spatial Pyramid --- p.23Chapter 2.5 --- Image Pre-processing --- p.24Chapter 2.5.1 --- Feature Extraction --- p.24Chapter 2.5.2 --- Spatial Filtering --- p.27Chapter 2.5.3 --- Local Enhancement --- p.31Chapter 2.5.4 --- Dynamic Range Stretching or Compression --- p.32Chapter 2.5.5 --- YIQ Color Model --- p.33Chapter 3 --- Model-based Pose Estimation --- p.35Chapter 3.1 --- Previous Work --- p.35Chapter 3.1.1 --- Estimation from Established Correspondences --- p.36Chapter 3.1.2 --- Direct Estimation from Image Intensities --- p.49Chapter 3.1.3 --- Perspective-3-Point Problem --- p.51Chapter 3.2 --- Our Iterative P3P Algorithm --- p.58Chapter 3.2.1 --- Gauss-Newton Method --- p.60Chapter 3.2.2 --- Dealing with Ambiguity --- p.61Chapter 3.2.3 --- 3D-to-3D Motion Estimation --- p.66Chapter 3.3 --- Experimental Results --- p.68Chapter 3.3.1 --- Synthetic Data --- p.68Chapter 3.3.2 --- Real Images --- p.72Chapter 3.4 --- Discussions --- p.73Chapter 4 --- Panoramic View Analysis --- p.76Chapter 4.1 --- Advanced Mosaic Representation --- p.76Chapter 4.1.1 --- Frame Alignment Policy --- p.77Chapter 4.1.2 --- Multi-resolution Representation --- p.77Chapter 4.1.3 --- Parallax-based Representation --- p.78Chapter 4.1.4 --- Multiple Moving Objects --- p.79Chapter 4.1.5 --- Layers and Tiles --- p.79Chapter 4.2 --- Panorama Construction --- p.79Chapter 4.2.1 --- Image Acquisition --- p.80Chapter 4.2.2 --- Image Alignment --- p.82Chapter 4.2.3 --- Image Integration --- p.88Chapter 4.2.4 --- Significant Residual Estimation --- p.89Chapter 4.3 --- Advanced Alignment Algorithms --- p.90Chapter 4.3.1 --- Patch-based Alignment --- p.91Chapter 4.3.2 --- Global Alignment (Block Adjustment) --- p.92Chapter 4.3.3 --- Local Alignment (Deghosting) --- p.93Chapter 4.4 --- Mosaic Application --- p.94Chapter 4.4.1 --- Visualization Tool --- p.94Chapter 4.4.2 --- Video Manipulation --- p.95Chapter 4.5 --- Experimental Results --- p.96Chapter 5 --- Panoramic Walkthrough --- p.99Chapter 5.1 --- Problem Statement and Notations --- p.100Chapter 5.2 --- Previous Work --- p.101Chapter 5.2.1 --- 3D Modeling and Rendering --- p.102Chapter 5.2.2 --- Branching Movies --- p.103Chapter 5.2.3 --- Texture Window Scaling --- p.104Chapter 5.2.4 --- Problems with Simple Texture Window Scaling --- p.105Chapter 5.3 --- Our Walkthrough Approach --- p.106Chapter 5.3.1 --- Cylindrical Projection onto Image Plane --- p.106Chapter 5.3.2 --- Generating Intermediate Frames --- p.108Chapter 5.3.3 --- Occlusion Handling --- p.114Chapter 5.4 --- Experimental Results --- p.116Chapter 5.5 --- Discussions --- p.116Chapter 6 --- Conclusion --- p.121Chapter A --- Formulation of Fischler and Bolles' Method for P3P Problems --- p.123Chapter B --- Derivation of z1 and z3 in terms of z2 --- p.127Chapter C --- Derivation of e1 and e2 --- p.129Chapter D --- Derivation of the Update Rule for Gauss-Newton Method --- p.130Chapter E --- Proof of (λ1λ2-λ 4）>〉0 --- p.132Chapter F --- Derivation of φ and hi --- p.133Chapter G --- Derivation of w1j to w4j --- p.134Chapter H --- More Experimental Results on Panoramic Stitching Algorithms --- p.138Bibliography --- p.14

Robust and efficient robotic mapping

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 123-129).Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods.by Edwin B. Olson.Ph.D

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed

Rank classification of linear line structure in determining trifocal tensor.

Zhao, Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (p. 111-117) and index.Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Objective of the study --- p.2Chapter 1.3 --- Challenges and our approach --- p.4Chapter 1.4 --- Original contributions --- p.6Chapter 1.5 --- Organization of this dissertation --- p.6Chapter 2 --- Related Work --- p.9Chapter 2.1 --- Critical configuration for motion estimation and projective reconstruction --- p.9Chapter 2.1.1 --- Point feature --- p.9Chapter 2.1.2 --- Line feature --- p.12Chapter 2.2 --- Camera motion estimation --- p.14Chapter 2.2.1 --- Line tracking --- p.15Chapter 2.2.2 --- Determining camera motion --- p.19Chapter 3 --- Preliminaries on Three-View Geometry and Trifocal Tensor --- p.23Chapter 3.1 --- Projective spaces P3 and transformations --- p.23Chapter 3.2 --- The trifocal tensor --- p.24Chapter 3.3 --- Computation of the trifocal tensor-Normalized linear algorithm --- p.31Chapter 4 --- Linear Line Structures --- p.33Chapter 4.1 --- Models of line space --- p.33Chapter 4.2 --- Line structures --- p.35Chapter 4.2.1 --- Linear line space --- p.37Chapter 4.2.2 --- Ruled surface --- p.37Chapter 4.2.3 --- Line congruence --- p.38Chapter 4.2.4 --- Line complex --- p.38Chapter 5 --- Critical Configurations of Three Views Revealed by Line Correspondences --- p.41Chapter 5.1 --- Two-view degeneracy --- p.41Chapter 5.2 --- Three-view degeneracy --- p.42Chapter 5.2.1 --- Introduction --- p.42Chapter 5.2.2 --- Linear line space --- p.44Chapter 5.2.3 --- Linear ruled surface --- p.54Chapter 5.2.4 --- Linear line congruence --- p.55Chapter 5.2.5 --- Linear line complex --- p.57Chapter 5.3 --- Retrieving tensor in critical configurations --- p.60Chapter 5.4 --- Rank classification of non-linear line structures --- p.61Chapter 6 --- Camera Motion Estimation Framework --- p.63Chapter 6.1 --- Line extraction --- p.64Chapter 6.2 --- Line tracking --- p.65Chapter 6.2.1 --- Preliminary geometric tracking --- p.65Chapter 6.2.2 --- Experimental results --- p.69Chapter 6.3 --- Camera motion estimation framework using EKF --- p.71Chapter 7 --- Experimental Results --- p.75Chapter 7.1 --- Simulated data experiments --- p.75Chapter 7.2 --- Real data experiments --- p.76Chapter 7.2.1 --- Linear line space --- p.80Chapter 7.2.2 --- Linear ruled surface --- p.84Chapter 7.2.3 --- Linear line congruence --- p.84Chapter 7.2.4 --- Linear line complex --- p.91Chapter 7.3 --- Empirical observation: ruled plane for line transfer --- p.93Chapter 7.4 --- Simulation for non-linear line structures --- p.94Chapter 8 --- Conclusions and Future Work --- p.97Chapter 8.1 --- Summary --- p.97Chapter 8.2 --- Future work --- p.99Chapter A --- Notations --- p.101Chapter B --- Tensor --- p.103Chapter C --- Matrix Decomposition and Estimation Techniques --- p.104Chapter D --- MATLAB Files --- p.107Chapter D.1 --- Estimation matrix --- p.107Chapter D.2 --- Line transfer --- p.109Chapter D.3 --- Simulation --- p.10

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Mobile depth sensing technology and algorithms with application to occupational therapy healthcare

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK government is striving to shift its current healthcare delivery model from clini-cian–oriented services, to that of patient and self–care–oriented intervention strategies. It seeks to do so through Information Communication (ICT) and Computer Mediated Re-ality Technologies (CMRT) as a key strategy to overcome the ever–increasing scarcity of healthcare resources and costs. To this end, in the UK the use of paper–based information systems have exhibited their limitations in providing apposite care. At the national level, The Royal College of Occupational Therapists (RCOT) identify home visits and modifica-tions as key levers in a multifactorial health programme to evaluate interventions for older people with a history of falling or are identified as being prone to falling. Prescribing Assistive Equipment (AE) is one such mechanism that seeks to reduce the risk of falling whilst promoting the continued independence of physical dexterity and mobility in older adults at home. In the UK, the yearly cost of falls is estimated at £2.3 billion. Further evidence places a 30% to 60% abandonment rate on prescribed AE by and large due to a ‘poor fit’ and measurement inaccuracies. To remain aligned with the national strategy, and assist in the eradication of measurement inaccuracies, this thesis employs Mobile Depth Sensing and Motion Track-ing Devices (MDSMTDs) to assist OTs in in the process of digitally measuring the extrin-sic fall–risk factors for the provision of AE. The quintessential component in this assess-ment lies in the measurement of fittings and furniture items in the home. To digitise and aid in this process, the artefact presented in this thesis employs stereo computer–vision and camera calibration algorithms to extract edges in 3D space. It modifies the Sobel–Feldman convolution filter by reducing the magnitude response and employs the camera intrinsic parameters as a mechanism to calculate the distortion matrix for interpolation between the edges and the 3D point cloud. Further Augmented Reality User Experience (AR-UX) facets are provided to digitise current state of the art clinical guidance and over-lay its instructions onto the real world (i.e., 3D space). Empirical mixed methods assessment revealed that in terms of accuracy, the arte-fact exhibited enhanced performance gains over current paper–based guidance. In terms of accuracy consistency, the artefact can rectify measurement consistency inaccuracies, but there are still a wide range of factors that can influence the integrity of the point-cloud in respect of the device’s point-of-view, holding positions and measurement speed. To this end, OTs usability, and adoption preferences materialise in favour of the artefact. In conclusion, this thesis demonstrates that MDSMTDs are a promising alterna-tive to existing paper–based measurement practices as OTs appear to prefer the digital–based system and that they can take measurements more efficiently and accurately

Part-based Grouping and Recognition: A Model-Guided Approach

Institute of Perception, Action and BehaviourThe recovery of generic solid parts is a fundamental step towards the realization of general-purpose vision systems. This thesis investigates issues in grouping, segmentation and recognition of parts from two-dimensional edge images. A new paradigm of part-based grouping of features is introduced that bridges the classical grouping and model-based approaches with the purpose of directly recovering parts from real images, and part-like models are used that both yield low theoretical complexity and reliably recover part-plausible groups of features. The part-like models used are statistical point distribution models, whose training set is built using random deformable superellipse. The computational approach that is proposed to perform model-guided part-based grouping consists of four distinct stages. In the first stage, codons, contour portions of similar curvature, are extracted from the raw edge image. They are considered to be indivisible image features because they have the desirable property of belonging either to single parts or joints. In the second stage, small seed groups (currently pairs, but further extension are proposed) of codons are found that give enough structural information for part hypotheses to be created. The third stage consists in initialising and pre-shaping the models to all the seed groups and then performing a full fitting to a large neighbourhood of the pre-shaped model. The concept of pre-shaping to a few significant features is a relatively new concept in deformable model fitting that has helped to dramatically increase robustness. The initialisations of the part models to the seed groups is performed by the first direct least-square ellipse fitting algorithm, which has been jointly discovered during this research; a full theoretical proof of the method is provided. The last stage pertains to the global filtering of all the hypotheses generated by the previous stages according to the Minimum Description Length criterion: the small number of grouping hypotheses that survive this filtering stage are the most economical representation of the image in terms of the part-like models. The filtering is performed by the maximisation of a boolean quadratic function by a genetic algorithm, which has resulted in the best trade-off between speed and robustness. Finally, images of parts can have a pronounced 3D structure, with ends or sides clearly visible. In order to recover this important information, the part-based grouping method is extended by employing parametrically deformable aspects models which, starting from the initial position provided by the previous stages, are fitted to the raw image by simulated annealing. These models are inspired by deformable superquadrics but are built by geometric construction, which render them two order of magnitudes faster to generate than in previous works. A large number of experiments is provided that validate the approach and, since several new issues have been opened by it, some future work is proposed

A platform for automatic 3D object reconstruction through multi-view stereo techniques for mobile devices

The goal of the project aims to generate a 3D model thanks to a set of images of it. With this purpose in mind, the project is composed of a client-based Android application and a web server-based Java application to provided an integrated solution to generate the model thanks to existing software