Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Mathematics. / Vita. / Ph.D.

No description available.

In this thesis, we present a strongly coupled data fusion architecture within a Bayesian framework for modeling the bi-directional influences between the scene and object classification mechanisms. A number of psychophysical studies provide experimental evidence that the object and the scene perception mechanisms are not functionally separate in the human visual system. Object recognition facilitates the recognition of the scene background and also knowledge of the scene context facilitates the recognition of the individual objects in the scene. The evidence indicating a bi-directional exchange between the two processes has motivated us to build a computational model where object and scene classification proceed in an interdependent manner, while no hierarchical relationship is imposed between the two processes. We propose a strongly coupled data fusion model for implementing the feedback relationship between the scene and object classification processes. We present novel schemes for modifying the Bayesian solutions for the scene and object classification tasks which allow data fusion between the two modules based on the constraining of the priors or the likelihoods. We have implemented and tested the two proposed models using a database of natural images created for this purpose. The Receiver Operator Curves (ROC) depicting the scene classification performance of the likelihood coupling and the prior coupling models show that scene classification performance improves significantly in both models as a result of the strong coupling of the scene and object modules. / ROC curves depicting the scene classification performance of the two models also show that the likelihood coupling model achieves a higher detection rate compared to the prior coupling model. We have also computed the average rise times of the models' outputs as a measure of comparing the speed of the two models. The results show that the likelihood coupling model outputs have a shorter rise time. Based on these experimental findings one can conclude that imposing constraints on the likelihood models provides better solutions to the scene classification problems compared to imposing constraints on the prior models. / We have also proposed an attentional feature modulation scheme, which consists of tuning the input image responses to the bank of Gabor filters based on the scene class probabilities estimated by the model and the energy profiles of the Gabor filters for different scene categories. Experimental results based on combining the attentional feature tuning scheme with the likelihood coupling and the prior coupling methods show a significant improvement in the scene classification performances of both models.

Yu, Tsz Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 95-100). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Surveillance and Computer Vision --- p.3 / Chapter 1.2 --- The Need for Abnormal Behavior Detection --- p.3 / Chapter 1.2.1 --- The Motivation --- p.3 / Chapter 1.2.2 --- Choosing the Right Surveillance Target --- p.5 / Chapter 1.3 --- Abnormal Behavior Detection: An Overview --- p.6 / Chapter 1.3.1 --- Challenges in Detecting Abnormal Behaviors --- p.6 / Chapter 1.3.2 --- Limitations of Existing Approaches --- p.8 / Chapter 1.3.3 --- New Design Concepts --- p.9 / Chapter 1.3.4 --- Requirements for Abnormal Behavior Detection --- p.10 / Chapter 1.4 --- Contributions --- p.11 / Chapter 1.4.1 --- An Unsupervised Experience-based Approach for Abnormal Behavior Detection --- p.11 / Chapter 1.4.2 --- Motion Histogram Transform: A Novel Feature Descriptors --- p.12 / Chapter 1.4.3 --- Real-time Algorithm for Abnormal Behavior Detection --- p.12 / Chapter 1.5 --- Thesis Organization --- p.13 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- From Segmentation to Visual Tracking --- p.14 / Chapter 2.1.1 --- Environment Modeling and Segmentation --- p.15 / Chapter 2.1.2 --- Spatial-temporal Feature Extraction --- p.18 / Chapter 2.2 --- Detecting Irregularities in Videos --- p.21 / Chapter 2.2.1 --- Model-based Method --- p.22 / Chapter 2.2.2 --- Non Model-based Method --- p.26 / Chapter 3 --- Design Framework --- p.29 / Chapter 3.1 --- Dynamic Scene and Behavior Model --- p.30 / Chapter 3.1.1 --- Images Sequences and Video --- p.30 / Chapter 3.1.2 --- Motions and Behaviors in Video --- p.31 / Chapter 3.1.3 --- Discovering Abnormal Behavior --- p.32 / Chapter 3.1.4 --- Problem Definition --- p.33 / Chapter 3.1.5 --- System Assumption --- p.34 / Chapter 3.2 --- Methodology --- p.35 / Chapter 3.2.1 --- Potential Improvements --- p.35 / Chapter 3.2.2 --- The Design Framework --- p.36 / Chapter 4 --- Implementation --- p.40 / Chapter 4.1 --- Preprocessing --- p.40 / Chapter 4.1.1 --- Data Input --- p.41 / Chapter 4.1.2 --- Motion Detection --- p.41 / Chapter 4.1.3 --- The Gaussian Mixture Background Model --- p.43 / Chapter 4.2 --- Feature Extraction --- p.46 / Chapter 4.2.1 --- Optical Flow Estimation --- p.47 / Chapter 4.2.2 --- Motion Histogram Transforms --- p.53 / Chapter 4.3 --- Feedback Learning --- p.56 / Chapter 4.3.1 --- The Observation Matrix --- p.58 / Chapter 4.3.2 --- Eigenspace Transformation --- p.58 / Chapter 4.3.3 --- Self-adaptive Update Scheme --- p.61 / Chapter 4.3.4 --- Summary --- p.62 / Chapter 4.4 --- Classification --- p.63 / Chapter 4.4.1 --- Detecting Abnormal Behavior via Statistical Saliencies --- p.64 / Chapter 4.4.2 --- Determining Feedback --- p.65 / Chapter 4.5 --- Localization and Output --- p.66 / Chapter 4.6 --- Conclusion --- p.69 / Chapter 5 --- Experiments --- p.71 / Chapter 5.1 --- Experiment Setup --- p.72 / Chapter 5.2 --- A Summary of Experiments --- p.74 / Chapter 5.3 --- Experiment Results: Part 1 --- p.78 / Chapter 5.4 --- Experiment Results: Part 2 --- p.81 / Chapter 5.5 --- Experiment Results: Part 3 --- p.83 / Chapter 5.6 --- Experiment Results: Part 4 --- p.86 / Chapter 5.7 --- Analysis and Conclusion --- p.86 / Chapter 6 --- Conclusions --- p.88 / Chapter 6.1 --- Application Extensions --- p.88 / Chapter 6.2 --- Limitations --- p.89 / Chapter 6.2.1 --- Surveillance Range --- p.89 / Chapter 6.2.2 --- Preparation Time for the System --- p.89 / Chapter 6.2.3 --- Calibration of Background Model --- p.90 / Chapter 6.2.4 --- Instability of Optical Flow Feature Extraction --- p.91 / Chapter 6.2.5 --- Lack of 3D information --- p.91 / Chapter 6.2.6 --- Dealing with Complex Behavior Patterns --- p.92 / Chapter 6.2.7 --- Potential Improvements --- p.92 / Chapter 6.2.8 --- New Method for Classification --- p.93 / Chapter 6.2.9 --- Introduction of Dynamic Texture as a Feature --- p.93 / Chapter 6.2.10 --- Using Multiple-camera System --- p.93 / Chapter 6.3 --- Summary --- p.94 / Bibliography --- p.95

Research on Multiple Object Tracking (MOT) has typically involved 2D displays where stimuli move in a single depth plane. However, under natural conditions, objects move in 3D which adds complexity to tracking. According to the spatial interference model, tracked objects have an inhibitory surround that when crossed causes tracking errors. How do these inhibitory fields translate to 3D space? Does multiple object tracking operate on a 2D planar projection, or is it in fact 3D? To investigate this, we used a fully immersive virtual-reality environment where participants were required to track 1 to 4 moving objects. We compared performance to a condition where participants viewed the same stimuli on a computer screen with monocular depth cues. Results suggest that participants were more accurate in the VR condition than the computer screen condition. This demonstrates interference is negligent when the objects are spatially distant, yet proximate within the 2D projection. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Lee, Hiu Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 80-82). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

The first approach, described in the first part of this thesis, studies 3D face modeling from multi-views. Today human face modeling and animation techniques are widely used to generate virtual characters and models. Such characters and models are used in movies, computer games, advertising, news broadcasting and other activities. We propose an efficient method to estimate the poses, the global shape and the local structures of a human head recorded in multiple face images or a video sequence by using a generic wireframe face model. Based on this newly proposed method, we have successfully developed a pose invariant face recognition system and a pose invariant face contour extraction method. / The objective of this thesis is to model and render complex scenes or objects from multiple images taken from different viewpoints. Two approaches to achieve this objective were investigated in this thesis. The first one is for known objects with prior geometrical models, which can be deformed to match the objects recorded in multiple input images. The second one is for general scenes or objects without prior geometrical models. / The proposed algorithms in this thesis were tested on many real and synthetic data. The experimental results illustrate their efficiency and limitations. / The second approach, described in the second part of this thesis, investigates 3D modeling and rendering for general complex scenes. The entertainment industry touches hundreds of millions of people every day, and synthetic pictures and 3D reconstruction of real scenes, often mixed with actual film footage, are now common place in computer games, sports broadcasting, TV advertising and feature films. A series of techniques has been developed to complete this task. First, a new view-ordering algorithm was proposed to organize and order an unorganized image database. Second, a novel and efficient multiview feature matching approach was developed to calibrate and track all views. Finally, both match propagation based and Bayesian based methods were developed to produce 3D scene models for rendering. / Yao Jian. / "September 2006." / Adviser: Wai-Kuen Chan. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1849. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 170-181). / 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. / Abstracts in English and Chinese. / School code: 1307.

捕捉攝像機運動和重建攝像機成像場景深度圖的測定是在計算機視覺和機器任務包括可視化控制和自主導航是非常重要。在執行上述任務時，一個攝像機（或攝像機群組）通常安裝在機器的執行端部。攝像機和執行端部之間的手眼校準在視覺控制的正常操作中是不可缺少的。同樣，在對於需要使用多個攝像機的情况下，它們的相對幾何關係也是對各種計算機視覺應用來說也是非常重要。 / 攝像機和場景的相對運動通常產生出optical flow。問題的困難主要在於，在直接觀察視頻中的optical flow通常不是完全由運動誘導出的optical flow，而只是它的一部分。這個部分就是空間圖像等光線輪廓的正交。這部分的流場被稱為normal flow。本論文提出直接利用normal flow，而不是由normal flow引申出的optical flow，去解決以下的問題：尋找攝像機運動，場景深度圖和手眼校準。這種方法有許多顯著的貢獻，它不需引申流場，進而不要求平滑的成像場景。跟optical flow相反，normal flow不需要複雜的優化處理程序去解決流場不連續性的問題，這種技術一般是需要用大量的計算量。這也打破了傳統攝像機運動與場景深度之間的問題，在沒有預先知道不連續位置的情況下也可找出攝像機的運動。這篇論提出了幾個直接方法運用在三種不同類型的視覺系統，分別是單個攝像機，雙攝像機和多個攝像機，去找出攝像機的運動。 / 本論文首先提通過Apparent Flow 正深度 (AFPD) 約束去利用所有觀察到的normal flow去找出單個攝像機的運動參數。AFPD約束是利用一個優化問題來估計運動參數。一個反复由粗到細雙重約束的投票框架能使AFPD約束尋找出運動參數。 / 由於有限的視頻採樣率，normal flow在提取方向比其幅度部分更準確。本論文提出了兩個約束條件：一個是Apparent Flow方向（AFD）的約束，另外一個是Apparent Flow 幅度（AFM）的約束去尋找運動參數。第一個約束本身是作為一個線性不等式系統去約束運動方向的參數，第二個是利用所有圖像位置的旋轉幅度的統一性去進一步限制運動參數。一個兩階段從粗到細的約束框架能使AFD及AFM約束尋找出運動參數。 / 然而，如果沒有optical flow，normal flow是唯一的原始資料，它通常遭受到有限影像分辨率和有限視頻採樣率的問題而產生出錯誤。本文探討了這個問題的補救措施，方法是把一些攝像機併在一起，形成一個近似球形的攝像機，以增加成像系統的視野。有了一個加寬視野，normal flow的數量可更大，這可以用來抵銷normal flow在每個成像點的提取錯誤。更重要的是，攝像頭的平移和旋轉運動方向可以透過Apparent Flow分離 (AFS) 約束 及 延伸Apparent Flow分離 (EAFS) 約束來獨立估算。 / 除了使用單攝像機或球面成像系統之外，立體視覺成像系統提供了其它的視覺線索去尋找攝像機在沒有被任意縮放大小的平移運動和深度圖。傳統的立體視覺方法是確定在兩個輸入圖像特徵的對應。然而，對應的建立是非常困難。本文探討了兩個直接方法來恢復完整的攝像機運動，而沒有需要利用一對影像明確的點至點對應。第一種方法是利用AFD和AFM約束伸延到立體視覺系統，並提供了一個穩定的幾何方法來確定平移運動的幅度。第二個方法需要利用有一個較大的重疊視場，以提供一個不需反覆計算的closed-form算法。一旦確定了運動參數，深度圖可以沒有任何困難地重建。從normal flow產生的深度圖一般是以稀疏的形式存在。我們可以通過擴張深度圖，然後利用它作為在常見的TV-L₁框架的初始估計。其結果不僅有一個更好的重建性能，也產生出更快的運算時間。 / 手眼校準通常是基於像圖特徵對應。本文提出一個替代方法，是從動態攝像系統產生的normal flow來做自我校準。為了使這個方法有更強防備噪音的能力，策略是使用normal flow的流場方向去尋找手眼幾何的方向部份。偏離點及部分的手眼幾何可利用normal flow固有的流場屬性去尋找。最後完整的手眼幾何可使用穩定法來變得更可靠。手眼校準還可以被用來確定多個攝像機的相對幾何關係，而不需要求它們有重疊的視場。 / Determination of general camera motion and reconstructing depth map from a captured video of the imaged scene relative to a camera is important for computer vision and various robotics tasks including visual control and autonomous navigation. A camera (or a cluster of cameras) is usually mounted on the end-effector of a robot arm when performing the above tasks. The determination of the relative geometry between the camera frame and the end-effector frame which is commonly referred as hand-eye calibration is essential to proper operation in visual control. Similarly, determining the relative geometry of multiple cameras is also important to various applications requiring the use of multi-camera rig. / The relative motion between an observer and the imaged scene generally induces apparent flow in the video. The difficulty of the problem lies mainly in that the flow pattern directly observable in the video is generally not the full flow field induced by the motion, but only partial information of it, which is orthogonal to the iso-brightness contour of the spatial image intensity profile. The partial flow field is known as the normal flow field. This thesis addresses several important problems in computer vision: determination of camera motion, recovery of depth map, and performing hand-eye calibration from the apparent flow (normal flow) pattern itself in the video data directly but not from the full flow interpolated from the apparent flow. This approach has a number of significant contributions. It does not require interpolating the flow field and in turn does not demand the imaged scene to be smooth. In contrast to optical flow, no sophisticated optimization procedures that account for handling flow discontinuities are required, and such techniques are generally computational expensive. It also breaks the classical chicken-and-egg problem between scene depth and camera motion. No prior knowledge about the locations of the discontinuities is required for motion determination. In this thesis, several direct methods are proposed to determine camera motion using three different types of imaging systems, namely monocular camera, stereo camera, and multi-camera rig. / This thesis begins with the Apparent Flow Positive Depth (AFPD) constraint to determine the motion parameters using all observable normal flows from a monocular camera. The constraint presents itself as an optimization problem to estimate the motion parameters. An iterative process in a constrained dual coarse-to-fine voting framework on the motion parameter space is used to exploit the constraint. / Due to the finite video sampling rate, the extracted normal flow field is generally more accurate in direction component than its magnitude part. This thesis proposes two constraints: one related to the direction component of the normal flow field - the Apparent Flow Direction (AFD) constraint, and the other to the magnitude component of the field - the Apparent Flow Magnitude (AFM) constraint, to determine motion. The first constraint presents itself as a system of linear inequalities to bind the direction of motion parameters; the second one uses the globality of rotational magnitude to all image positions to constrain the motion parameters further. A two-stage iterative process in a coarse-to-fine framework on the motion parameter space is used to exploit the two constraints. / Yet without the need of the interpolation step, normal flow is only raw information extracted locally that generally suffers from flow extraction error arisen from finiteness of the image resolution and video sampling rate. This thesis explores a remedy to the problem, which is to increase the visual field of the imaging system by fixating a number of cameras together to form an approximate spherical eye. With a substantially widened visual field, the normal flow data points would be in a much greater number, which can be used to combat the local flow extraction error at each image point. More importantly, the directions of translation and rotation components in general motion can be separately estimated with the use of the novel Apparent Flow Separation (AFS) and Extended Apparent Flow Separation (EAFS) constraints. / Instead of using a monocular camera or a spherical imaging system, stereo vision contributes another visual clue to determine magnitude of translation and depth map without the problem of arbitrarily scaling of the magnitude. The conventional approach in stereo vision is to determine feature correspondences across the two input images. However, the correspondence establishment is often difficult. This thesis explores two direct methods to recover the complete camera motion from the stereo system without the explicit point-to-point correspondences matching. The first method extends the AFD and AFM constraints to stereo camera, and provides a robust geometrical method to determine translation magnitude. The second method which requires the stereo image pair to have a large overlapped field of view provides a closed-form solution, requiring no iterative computation. Once the motion parameters are here, depth map can be reconstructed without any difficulty. The depth map resulted from normal flows is generally sparse in nature. We can interpolate the depth map and then utilizing it as an initial estimate in a conventional TV-L₁ framework. The result is not only a better reconstruction performance, but also a faster computation time. / Calibration of hand-eye geometry is usually based on feature correspondences. This thesis presents an alternative method that uses normal flows generated from an active camera system to perform self-calibration. In order to make the method more robust to noise, the strategy is to use the direction component of the flow field which is more noise-immune to recover the direction part of the hand-eye geometry first. Outliers are then detected using some intrinsic properties of the flow field together with the partially recovered hand-eye geometry. The final solution is refined using a robust method. The method can also be used to determine the relative geometry of multiple cameras without demanding overlap in their visual fields. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Hui, Tak Wai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 159-165). / Abstracts in English and Chinese. / Acknowledgements --- p.i / Abstract --- p.ii / Lists of Figures --- p.xiii / Lists of Tables --- p.xix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Research Objectives --- p.6 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter Chapter 2 --- Literature Review --- p.10 / Chapter 2.1 --- Introduction --- p.10 / Chapter 2.2 --- Recovery of Optical Flows --- p.10 / Chapter 2.3 --- Egomotion Estimation Based on Optical Flow Field --- p.14 / Chapter 2.3.1 --- Bilinear Constraint --- p.14 / Chapter 2.3.2 --- Subspace Method --- p.15 / Chapter 2.3.3 --- Partial Search Method --- p.16 / Chapter 2.3.4 --- Fixation --- p.17 / Chapter 2.3.5 --- Region Alignment --- p.17 / Chapter 2.3.6 --- Linearity and Divergence Properties of Optical Flows --- p.18 / Chapter 2.3.7 --- Constraint Lines and Collinear Points --- p.18 / Chapter 2.3.8 --- Multi-Camera Rig --- p.19 / Chapter 2.3.9 --- Discussion --- p.21 / Chapter 2.4 --- Determining Egomotion Using Direct Methods --- p.22 / Chapter 2.4.1 --- Introduction --- p.22 / Chapter 2.4.2 --- Classical Methods --- p.23 / Chapter 2.4.3 --- Pattern Matching --- p.24 / Chapter 2.4.4 --- Search Subspace Method --- p.25 / Chapter 2.4.5 --- Histogram-Based Method --- p.26 / Chapter 2.4.6 --- Multi-Camera Rig --- p.26 / Chapter 2.4.7 --- Discussion --- p.27 / Chapter 2.5 --- Determining Egomotion Using Feature Correspondences --- p.28 / Chapter 2.6 --- Hand-Eye Calibration --- p.30 / Chapter 2.7 --- Summary --- p.31 / Chapter Chapter 3 --- Determining Motion from Monocular Camera Using Merely the Positive Depth Constraint --- p.32 / Chapter 3.1 --- Introduction --- p.32 / Chapter 3.2 --- Related Works --- p.33 / Chapter 3.3 --- Background --- p.34 / Chapter 3.3 --- Apparent Flow Positive Depth (AFPD) Constraint --- p.39 / Chapter 3.4 --- Numerical Solution to AFPD Constraint --- p.40 / Chapter 3.5 --- Constrained Coarse-to-Fine Searching --- p.40 / Chapter 3.6 --- Experimental Results --- p.43 / Chapter 3.7 --- Conclusion --- p.47 / Chapter Chapter 4 --- Determining Motion from Monocular Camera Using Direction and Magnitude of Normal Flows Separately --- p.48 / Chapter 4.1 --- Introduction --- p.48 / Chapter 4.2 --- Related Works --- p.50 / Chapter 4.3 --- Apparent Flow Direction (AFD) Constraint --- p.51 / Chapter 4.3.1 --- The Special Case: Pure Translation --- p.51 / Chapter 4.3.1.1 --- Locus of Translation Using Full Flow as a Constraint --- p.51 / Chapter 4.3.1.2 --- Locus of Translation Using Normal Flow as a Constraint --- p.53 / Chapter 4.3.2 --- The Special Case: Pure Rotation --- p.54 / Chapter 4.3.2.1 --- Locus of Rotation Using Full Flow as a Constraint --- p.54 / Chapter 4.3.2.2 --- Locus of Rotation Using Normal Flow as a Constraint --- p.54 / Chapter 4.3.3 --- Solving the System of Linear Inequalities for the Two Special Cases --- p.55 / Chapter 4.3.5 --- Ambiguities of AFD Constraint --- p.59 / Chapter 4.4 --- Apparent Flow Magnitude (AFM) Constraint --- p.60 / Chapter 4.5 --- Putting the Two Constraints Together --- p.63 / Chapter 4.6 --- Experimental Results --- p.65 / Chapter 4.6.1 --- Simulation --- p.65 / Chapter 4.6.2 --- Video Data --- p.67 / Chapter 4.6.2.1 --- Pure Translation --- p.67 / Chapter 4.6.2.2 --- General Motion --- p.68 / Chapter 4.7 --- Conclusion --- p.72 / Chapter Chapter 5 --- Determining Motion from Multi-Cameras with Non-Overlapping Visual Fields --- p.73 / Chapter 5.1 --- Introduction --- p.73 / Chapter 5.2 --- Related Works --- p.75 / Chapter 5.3 --- Background --- p.76 / Chapter 5.3.1 --- Image Sphere --- p.77 / Chapter 5.3.2 --- Planar Case --- p.78 / Chapter 5.3.3 --- Projective Transformation --- p.79 / Chapter 5.4 --- Constraint from Normal Flows --- p.80 / Chapter 5.5 --- Approximation of Spherical Eye by Multiple Cameras --- p.81 / Chapter 5.6 --- Recovery of Motion Parameters --- p.83 / Chapter 5.6.1 --- Classification of a Pair of Normal Flows --- p.84 / Chapter 5.6.2 --- Classification of a Triplet of Normal Flows --- p.86 / Chapter 5.6.3 --- Apparent Flow Separation (AFS) Constraint --- p.87 / Chapter 5.6.3.1 --- Constraint to Direction of Translation --- p.87 / Chapter 5.6.3.2 --- Constraint to Direction of Rotation --- p.88 / Chapter 5.6.3.3 --- Remarks about the AFS Constraint --- p.88 / Chapter 5.6.4 --- Extension of Apparent Flow Separation Constraint (EAFS) --- p.89 / Chapter 5.6.4.1 --- Constraint to Direction of Translation --- p.90 / Chapter 5.6.4.2 --- Constraint to Direction of Rotation --- p.92 / Chapter 5.6.5 --- Solution to the AFS and EAFS Constraints --- p.94 / Chapter 5.6.6 --- Apparent Flow Magnitude (AFM) Constraint --- p.96 / Chapter 5.7 --- Experimental Results --- p.98 / Chapter 5.7.1 --- Simulation --- p.98 / Chapter 5.7.2 --- Real Video --- p.103 / Chapter 5.7.2.1 --- Using Feature Correspondences --- p.108 / Chapter 5.7.2.2 --- Using Optical Flows --- p.108 / Chapter 5.7.2.3 --- Using Direct Methods --- p.109 / Chapter 5.8 --- Conclusion --- p.111 / Chapter Chapter 6 --- Motion and Shape from Binocular Camera System: An Extension of AFD and AFM Constraints --- p.112 / Chapter 6.1 --- Introduction --- p.112 / Chapter 6.2 --- Related Works --- p.112 / Chapter 6.3 --- Recovery of Camera Motion Using Search Subspaces --- p.113 / Chapter 6.4 --- Correspondence-Free Stereo Vision --- p.114 / Chapter 6.4.1 --- Determination of Full Translation Using Two 3D Lines --- p.114 / Chapter 6.4.2 --- Determination of Full Translation Using All Normal Flows --- p.115 / Chapter 6.4.3 --- Determination of Full Translation Using a Geometrical Method --- p.117 / Chapter 6.5 --- Experimental Results --- p.119 / Chapter 6.5.1 --- Synthetic Image Data --- p.119 / Chapter 6.5.2 --- Real Scene --- p.120 / Chapter 6.6 --- Conclusion --- p.122 / Chapter Chapter 7 --- Motion and Shape from Binocular Camera System: A Closed-Form Solution for Motion Determination --- p.123 / Chapter 7.1 --- Introduction --- p.123 / Chapter 7.2 --- Related Works --- p.124 / Chapter 7.3 --- Background --- p.125 / Chapter 7.4 --- Recovery of Camera Motion Using a Linear Method --- p.126 / Chapter 7.4.1 --- Region-Correspondence Stereo Vision --- p.126 / Chapter 7.3.2 --- Combined with Epipolar Constraints --- p.127 / Chapter 7.4 --- Refinement of Scene Depth --- p.131 / Chapter 7.4.1 --- Using Spatial and Temporal Constraints --- p.131 / Chapter 7.4.2 --- Using Stereo Image Pairs --- p.134 / Chapter 7.5 --- Experiments --- p.136 / Chapter 7.5.1 --- Synthetic Data --- p.136 / Chapter 7.5.2 --- Real Image Sequences --- p.137 / Chapter 7.6 --- Conclusion --- p.143 / Chapter Chapter 8 --- Hand-Eye Calibration Using Normal Flows --- p.144 / Chapter 8.1 --- Introduction --- p.144 / Chapter 8.2 --- Related Works --- p.144 / Chapter 8.3 --- Problem Formulation --- p.145 / Chapter 8.3 --- Model-Based Brightness Constraint --- p.146 / Chapter 8.4 --- Hand-Eye Calibration --- p.147 / Chapter 8.4.1 --- Determining the Rotation Matrix R --- p.148 / Chapter 8.4.2 --- Determining the Direction of Position Vector T --- p.149 / Chapter 8.4.3 --- Determining the Complete Position Vector T --- p.150 / Chapter 8.4.4 --- Extrinsic Calibration of a Multi-Camera Rig --- p.151 / Chapter 8.5 --- Experimental Results --- p.151 / Chapter 8.5.1 --- Synthetic Data --- p.151 / Chapter 8.5.2 --- Real Image Data --- p.152 / Chapter 8.6 --- Conclusion --- p.153 / Chapter Chapter 9 --- Conclusion and Future Work --- p.154 / Related Publications --- p.158 / Bibliography --- p.159 / Appendix --- p.166 / Chapter A --- Apparent Flow Direction Constraint --- p.166 / Chapter B --- Ambiguity of AFD Constraint --- p.168 / Chapter C --- Relationship between the Angle Subtended by any two Flow Vectors in Image Plane and the Associated Flow Vectors in Image Sphere --- p.169

Lam, Kin Kwok. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 99-103). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Figures --- p.viii / List of Tables --- p.xi / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Intrinsic Problem of Today's Pose Estimation Systems --- p.1 / Chapter 1.2 --- Multi-sensors Data Fusion --- p.2 / Chapter 1.3 --- Objectives and Contributions --- p.3 / Chapter 1.4 --- Organization of the dissertation --- p.4 / Chapter Chapter 2 --- Architecture of Sensing System --- p.5 / Chapter 2.1 --- Hardware for Pose Estimation System --- p.5 / Chapter 2.2 --- Software for Pose Estimation System --- p.6 / Chapter Chapter 3 --- Inertial Measurement System --- p.7 / Chapter 3.1 --- Basic knowledge of Inertial Measurement System --- p.7 / Chapter 3.2 --- Strapdown Inertial Navigation --- p.8 / Chapter 3.2.1 --- Tracking Orientation --- p.9 / Chapter 3.2.2 --- Discussion of Attitude Representations --- p.14 / Chapter 3.2.3 --- Tracking Position --- p.16 / Chapter 3.3 --- Summary of Strapdown Inertial Navigation --- p.16 / Chapter Chapter 4 --- Visual Tracking System --- p.17 / Chapter 4.1 --- Background of Visual Tracking System --- p.17 / Chapter 4.2 --- Basic knowledge of Camera Calibration and Model --- p.18 / Chapter 4.2.1 --- Related Coordinate Frames --- p.18 / Chapter 4.2.2 --- Pinhole Camera Model --- p.20 / Chapter 4.2.3 --- Calibration for Nonlinear Model --- p.21 / Chapter 4.3 --- Implementation of Process to Calibrate Camera --- p.22 / Chapter 4.3.1 --- Image Capture and Corners Extraction --- p.22 / Chapter 4.3.2 --- Camera Calibration --- p.23 / Chapter 4.4 --- Perspective-n-Point Problem --- p.25 / Chapter 4.5 --- Camera Pose Estimation Algorithms --- p.26 / Chapter 4.5.1 --- Pose Estimation Using Quadrangular Targets --- p.27 / Chapter 4.5.2 --- Efficient Perspective-n-Point Camera Pose Estimation --- p.31 / Chapter 4.5.3 --- Linear N-Point Camera Pose Determination --- p.33 / Chapter 4.5.4 --- Pose Estimation from Orthography and Scaling with Iterations --- p.36 / Chapter 4.6 --- Experimental Results of Camera Pose Estimation Algorithms --- p.40 / Chapter 4.6.1 --- Simulation Test --- p.40 / Chapter 4.6.2 --- Real Images Test --- p.43 / Chapter 4.6.3 --- Summary --- p.46 / Chapter Chapter 5 --- Kalman Filter --- p.47 / Chapter 5.1 --- Linear Dynamic System Model --- p.48 / Chapter 5.2 --- Time Update --- p.48 / Chapter 5.3 --- Measurement Update --- p.49 / Chapter 5.3.1 --- Maximum a Posterior Probability --- p.49 / Chapter 5.3.2 --- Batch Least-Square Estimation --- p.51 / Chapter 5.3.3 --- Measurement Update in Kalman Filter --- p.54 / Chapter 5.4 --- Summary of Kalman Filter --- p.56 / Chapter Chapter 6 --- Extended Kalman Filter --- p.58 / Chapter 6.1 --- Linearization of Nonlinear Systems --- p.58 / Chapter 6.2 --- Extended Kalman Filter --- p.59 / Chapter Chapter 7 --- Unscented Kalman Filter --- p.61 / Chapter 7.1 --- Least-square Estimator Structure --- p.61 / Chapter 7.2 --- Unscented Transform --- p.62 / Chapter 7.3 --- Unscented Kalman Filter --- p.64 / Chapter Chapter 8 --- Data Fusion Algorithm --- p.68 / Chapter 8.1 --- Traditional Multi-Sensor Data Fusion --- p.69 / Chapter 8.1.1 --- Measurement Fusion --- p.69 / Chapter 8.1.2 --- Track-to-Track Fusion --- p.71 / Chapter 8.2 --- Multi-Sensor Data Fusion using Extended Kalman Filter --- p.72 / Chapter 8.2.1 --- Time Update Model --- p.73 / Chapter 8.2.2 --- Measurement Update Model --- p.74 / Chapter 8.3 --- Multi-Sensor Data Fusion using Unscented Kalman Filter --- p.75 / Chapter 8.3.1 --- Time Update Model --- p.75 / Chapter 8.3.2 --- Measurement Update Model --- p.76 / Chapter 8.4 --- Simulation Test --- p.76 / Chapter 8.5 --- Experimental Test --- p.80 / Chapter 8.5.1 --- Rotational Test --- p.81 / Chapter 8.5.2 --- Translational Test --- p.86 / Chapter Chapter 9 --- Future Work --- p.93 / Chapter 9.1 --- Zero Velocity Compensation --- p.93 / Chapter 9.1.1 --- Stroke Segmentation --- p.93 / Chapter 9.1.2 --- Zero Velocity Compensation (ZVC) --- p.94 / Chapter 9.1.3 --- Experimental Results --- p.94 / Chapter 9.2 --- Random Sample Consensus Algorithm (RANSAC) --- p.96 / Chapter Chapter 10 --- Conclusion --- p.97 / Bibliography --- p.99

An algorithmic method for the recognition of fungal spore cells in microscopic images, as well as its development and its origin, are described and demonstrated. The process is designed for a machine vision project which automatically identifies fungal spores within field samples for epidemiological simulation models. The method consists of a three-pass system that successfully recognizes spores in any position and which is tolerant of occlusion. / The algorithm, as implemented, demonstrated an accuracy of $ pm$5.3% on low quality images which is less than the assumed error of humans performing the same task. The processing speed also compared favorably with the performance of humans. / The method developed presents a framework of description that, through the first two passes, highlights certain distinctive aspects within an image. Those highlighted aspects are then recognized by the third pass. The system is loosely based on biological vision, is extremely versatile and could be adapted for the recognition of virtually any object in a digitized image.