Wang, Yingze. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 42-47). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.5 / Chapter 2.1 --- Line labeling and realization problem --- p.5 / Chapter 2.2 --- 3D reconstruction from multiple views --- p.6 / Chapter 2.3 --- 3D reconstruction from single line drawings --- p.7 / Chapter 2.3.1 --- Face identification from the line drawings --- p.7 / Chapter 2.3.2 --- 3D geometry reconstruction --- p.9 / Chapter 2.4 --- Our research topic and contributions --- p.13 / Chapter 3 --- Reconstruction of Curved Manifold Objects --- p.14 / Chapter 3.1 --- Assumptions and terminology --- p.14 / Chapter 3.2 --- Reconstruction of curved manifold objects --- p.17 / Chapter 3.2.1 --- Distinguishing between curved and planar faces --- p.17 / Chapter 3.2.2 --- Transformation of Line Drawings --- p.20 / Chapter 3.2.3 --- Regularities --- p.23 / Chapter 3.2.4 --- 3D Wireframe Reconstruction --- p.26 / Chapter 3.2.5 --- Generating Curved Faces --- p.28 / Chapter 3.2.6 --- The Complete 3D Reconstruction Algorithm --- p.33 / Chapter 4 --- Experiments --- p.35 / Chapter 5 --- Conclusions and Future Work --- p.40 / Chapter 5.1 --- Conclusions --- p.40 / Chapter 5.2 --- Future work --- p.40 / Bibliography --- p.42

跨模態圖像的合成和識別已成為計算機視覺領域的熱點。實際應用中存在各種各樣的圖像模態，比如刑偵中使用的素描畫和光照不變人臉識別中使用的近紅外圖像。由於某些模態的圖像很難獲得，模態間的轉換和匹配是一項十分有用的技術，為計算機視覺的應用提供了很大的便利。 / 本論文研究了三個應用：人像素描畫的合成，基於樣本的圖像風格化和人像素描畫識別。 / 我們將人像素描畫的合成的前沿研究擴展到非可控條件下的合成。以前的工作都只能在嚴格可控的條件下從照片合成素描畫。我們提出了一種魯棒的算法，可以從有光照和姿態變化的人臉照片合成素描畫。該算法用多尺度馬爾可夫隨機場來合成局部素描圖像塊。對光照和姿態的魯棒性通過三個部分來實現：基於面部器官的形狀先驗可以抑制缺陷和扭曲的合成效果，圖像塊的特征描述子和魯棒的距離測度用來選擇素描圖像塊，以及像素灰度和梯度的一致性來有效地匹配鄰近的素描圖像塊。在CUHK人像素描數據庫和網上的名人照片上的實驗結果表明我們的算法顯著提高了現有算法的效果。 / 針對基於樣本的圖像風格化，我們提供了一種將模板圖像的藝術風格傳遞到照片上的有效方法。大多數已有方法沒有考慮圖像內容和風格的分離。我們提出了一種通過頻段分解的風格傳遞算法。一幅圖像被分解成低頻、中頻和高頻分量，分別描述內容、主要風格和邊緣信息。接著中頻和高頻分量中的風格從模板傳遞到照片，這一過程用馬爾可夫隨機場來建模。最後我們結合照片中的低頻分量和獲得的風格信息重建出藝術圖像。和其它算法相比，我們的方法不僅合成了風格，而且很好的保持了原有的圖像內容。我們通過圖像風格化和個性化藝術合成的實驗來驗證了算法的有效性。 / 我們為人像素描畫的識別提出了一個從數據中學習人臉描述子的新方向。最近的研究都集中在轉換照片和素描畫到相同的模態，或者設計復雜的分類算法來減少從照片和素描畫提取的特征的模態間差異。我們提出了一種新穎的方法：在提取特征的階段減小模態間差異。我們用一種基於耦合信息論編碼的人臉描述子來獲取有判別性的局部人臉結構和有效的匹配照片和素描畫。通過最大化在量化特征空間的照片和素描畫的互信息，我們設計了耦合信息論投影森林來實現耦合編碼。在世界上最大的人像素描畫數據庫上的結果表明我們的方法和已有最好的方法相比有顯著提高。 / Inter-modality image synthesis and recognition has been a hot topic in computer vision. In real-world applications, there are diverse image modalities, such as sketch images for law enforcement and near infrared images for illumination invariant face recognition. Therefore, it is often useful to transform images from a modality to another or match images from different modalities, due to the difficulty of acquiring image data in some modality. These techniques provide large flexibility for computer vision applications. / In this thesis we study three problems: face sketch synthesis, example-based image stylization, and face sketch recognition. / For face sketch synthesis, we expand the frontier to synthesis from uncontrolled face photos. Previous methods only work under well controlled conditions. We propose a robust algorithm for synthesizing a face sketch from a face photo with lighting and pose variations. It synthesizes local sketch patches using a multiscale Markov Random Field (MRF) model. The robustness to lighting and pose variations is achieved with three components: shape priors specific to facial components to reduce artifacts and distortions, patch descriptors and robust metrics for selecting sketch patch candidates, and intensity compatibility and gradient compatibility to match neighboring sketch patches effectively. Experiments on the CUHK face sketch database and celebrity photos collected from the web show that our algorithm significantly improves the performance of the state-of-the-art. / For example-based image stylization, we provide an effective approach of transferring artistic effects from a template image to photos. Most existing methods do not consider the content and style separately. We propose a style transfer algorithm via frequency band decomposition. An image is decomposed into the low-frequency (LF), mid-frequency (MF), and highfrequency( HF) components, which describe the content, main style, and information along the boundaries. Then the style is transferred from the template to the photo in the MF and HF components, which is formulated as MRF optimization. Finally a reconstruction step combines the LF component of the photo and the obtained style information to generate the artistic result. Compared to the other algorithms, our method not only synthesizes the style, but also preserves the image content well. We demonstrate that our approach performs excellently in image stylization and personalized artwork in experiments. / For face sketch recognition, we propose a new direction based on learning face descriptors from data. Recent research has focused on transforming photos and sketches into the same modality for matching or developing advanced classification algorithms to reduce the modality gap between features extracted from photos and sketches. We propose a novel approach by reducing the modality gap at the feature extraction stage. A face descriptor based on coupled information-theoretic encoding is used to capture discriminative local face structures and to effectively match photos and sketches. Guided by maximizing the mutual information between photos and sketches in the quantized feature spaces, the coupled encoding is achieved by the proposed coupled information-theoretic projection forest. Experiments on the largest face sketch database show that our approach significantly outperforms the state-of-the-art methods. / 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. / Zhang, Wei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 121-137). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Multi-Modality Computer Vision --- p.1 / Chapter 1.2 --- Face Sketches --- p.4 / Chapter 1.2.1 --- Face Sketch Synthesis --- p.6 / Chapter 1.2.2 --- Face Sketch Recognition --- p.7 / Chapter 1.3 --- Example-based Image Stylization --- p.9 / Chapter 1.4 --- Contributions and Summary of Approaches --- p.10 / Chapter 1.5 --- Thesis Road Map --- p.13 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- Related Works in Face Sketch Synthesis --- p.14 / Chapter 2.2 --- Related Works in Example-based Image Stylization --- p.17 / Chapter 2.3 --- Related Works in Face Sketch Recognition --- p.21 / Chapter 3 --- Lighting and Pose Robust Sketch Synthesis --- p.27 / Chapter 3.1 --- The Algorithm --- p.31 / Chapter 3.1.1 --- Overview of the Method --- p.32 / Chapter 3.1.2 --- Local Evidence --- p.34 / Chapter 3.1.3 --- Shape Prior --- p.40 / Chapter 3.1.4 --- Neighboring Compatibility --- p.42 / Chapter 3.1.5 --- Implementation Details --- p.43 / Chapter 3.1.6 --- Acceleration --- p.45 / Chapter 3.2 --- Experimental Results --- p.47 / Chapter 3.2.1 --- Lighting and Pose Variations --- p.49 / Chapter 3.2.2 --- Celebrity Faces from the Web --- p.54 / Chapter 3.3 --- Conclusion --- p.54 / Chapter 4 --- Style Transfer via Band Decomposition --- p.58 / Chapter 4.1 --- Introduction --- p.58 / Chapter 4.2 --- Algorithm Overview --- p.63 / Chapter 4.3 --- Image Style Transfer --- p.64 / Chapter 4.3.1 --- Band Decomposition --- p.64 / Chapter 4.3.2 --- MF and HF Component Processing --- p.67 / Chapter 4.3.3 --- Reconstruction --- p.74 / Chapter 4.4 --- Experiments --- p.76 / Chapter 4.4.1 --- Comparison to State-of-the-Art --- p.76 / Chapter 4.4.2 --- Extended Application: Personalized Artwork --- p.82 / Chapter 4.5 --- Conclusion --- p.84 / Chapter 5 --- Coupled Encoding for Sketch Recognition --- p.86 / Chapter 5.1 --- Introduction --- p.86 / Chapter 5.1.1 --- Related work --- p.89 / Chapter 5.2 --- Information-Theoretic Projection Tree --- p.90 / Chapter 5.2.1 --- Projection Tree --- p.91 / Chapter 5.2.2 --- Mutual Information Maximization --- p.92 / Chapter 5.2.3 --- Tree Construction with MMI --- p.94 / Chapter 5.2.4 --- Randomized CITP Forest --- p.102 / Chapter 5.3 --- Coupled Encoding Based Descriptor --- p.103 / Chapter 5.4 --- Experiments --- p.106 / Chapter 5.4.1 --- Descriptor Comparison --- p.108 / Chapter 5.4.2 --- Parameter Exploration --- p.109 / Chapter 5.4.3 --- Experiments on Benchmarks --- p.112 / Chapter 5.5 --- Conclusions --- p.115 / Chapter 6 --- Conclusion --- p.116 / Bibliography --- p.121

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

Du, Hao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 61). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- 3D Reconstruction from 2D Line Drawings and its Applications --- p.1 / Chapter 1.2 --- Algorithmic Development of 3D Reconstruction from 2D Line Drawings --- p.3 / Chapter 1.2.1 --- Line Labeling and Realization Problem --- p.4 / Chapter 1.2.2 --- 3D Reconstruction from Multiple Line Drawings --- p.5 / Chapter 1.2.3 --- 3D Reconstruction from a Single Line Drawing --- p.6 / Chapter 1.3 --- Research Problems and Our Contributions --- p.12 / Chapter 2 --- Adaptive Parameter Setting --- p.15 / Chapter 2.1 --- Regularities in Optimization-Based 3D Reconstruction --- p.15 / Chapter 2.1.1 --- Face Planarity --- p.18 / Chapter 2.1.2 --- Line Parallelism --- p.19 / Chapter 2.1.3 --- Line Verticality --- p.19 / Chapter 2.1.4 --- Isometry --- p.19 / Chapter 2.1.5 --- Corner Orthogonality --- p.20 / Chapter 2.1.6 --- Skewed Facial Orthogonality --- p.21 / Chapter 2.1.7 --- Skewed Facial Symmetry --- p.22 / Chapter 2.1.8 --- Line Orthogonality --- p.24 / Chapter 2.1.9 --- Minimum Standard Deviation of Angles --- p.24 / Chapter 2.1.10 --- Face Perpendicularity --- p.24 / Chapter 2.1.11 --- Line Collinearity --- p.25 / Chapter 2.1.12 --- Whole Symmetry --- p.25 / Chapter 2.2 --- Adaptive Parameter Setting in the Objective Function --- p.26 / Chapter 2.2.1 --- Hill-Climbing Optimization Technique --- p.28 / Chapter 2.2.2 --- Adaptive Weight Setting and its Explanations --- p.29 / Chapter 3 --- Parameter Learning --- p.33 / Chapter 3.1 --- Construction of A Large 3D Object Database --- p.33 / Chapter 3.2 --- Training Dataset Generation --- p.34 / Chapter 3.3 --- Parameter Learning Framework --- p.37 / Chapter 3.3.1 --- Evolutionary Algorithms --- p.38 / Chapter 3.3.2 --- Reconstruction Error Calculation --- p.39 / Chapter 3.3.3 --- Parameter Learning Algorithm --- p.41 / Chapter 4 --- Experimental Results --- p.45 / Chapter 4.1 --- Adaptive Parameter Setting --- p.45 / Chapter 4.1.1 --- Use Manually-Set Weights --- p.45 / Chapter 4.1.2 --- Learn the Best Weights with Different Strategies --- p.48 / Chapter 4.2 --- Evolutionary-Algorithm-Based Parameter Learning --- p.49 / Chapter 5 --- Conclusions and Future Work --- p.53 / Bibliography --- p.55

Li, Sai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-96). / Abstracts in English and Chinese. / Abstract --- p.2 / List of Figures --- p.6 / Acknowledgement --- p.10 / Chapter Chapter 1 --- Introduction --- p.11 / Chapter 1.1 --- Problem definition --- p.11 / Chapter 1.2. --- Proposed algorithm --- p.12 / Chapter 1.3. --- Main features --- p.14 / Chapter Chapter 2 --- Literature Review --- p.16 / Chapter 2.1 --- Local Feature Matching techniques --- p.16 / Chapter 2.2. --- Global Iterative alignment techniques --- p.19 / Chapter 2.3 --- Other Approaches --- p.20 / Chapter Chapter 3 --- Correspondence Matching --- p.21 / Chapter 3.1 --- Fundamental Techniques --- p.24 / Chapter 3.1.1 --- Geodesic Distance Approximation --- p.24 / Chapter 3.1.1.1 --- Dijkstra ´ةs algorithm --- p.25 / Chapter 3.1.1.2 --- Wavefront Propagation --- p.26 / Chapter 3.1.2 --- Farthest Point Sampling --- p.27 / Chapter 3.1.3 --- Curvature Estimation --- p.29 / Chapter 3.1.4 --- Radial Basis Function (RBF) --- p.32 / Chapter 3.1.5 --- Multi-dimensional Scaling (MDS) --- p.35 / Chapter 3.1.5.1 --- Classical MDS --- p.35 / Chapter 3.1.5.2 --- Fast MDS --- p.38 / Chapter 3.2 --- Matching Processes --- p.40 / Chapter 3.2.1 --- Posture Alignment --- p.42 / Chapter 3.2.1.1 --- Sign Flip Correction --- p.43 / Chapter 3.2.1.2 --- Input model Alignment --- p.49 / Chapter 3.2.2 --- Surface Fitting --- p.52 / Chapter 3.2.2.1 --- Optimizing Surface Fitness --- p.54 / Chapter 3.2.2.2 --- Optimizing Surface Smoothness --- p.56 / Chapter 3.2.3 --- Feature Matching Refinement --- p.59 / Chapter 3.2.3.1 --- Feature descriptor --- p.61 / Chapter 3.2.3.3 --- Feature Descriptor matching --- p.63 / Chapter Chapter 4 --- Experimental Result --- p.66 / Chapter 4.1 --- Result of the Fundamental Techniques --- p.66 / Chapter 4.1.1 --- Geodesic Distance Approximation --- p.67 / Chapter 4.1.2 --- Farthest Point Sampling (FPS) --- p.67 / Chapter 4.1.3 --- Radial Basis Function (RBF) --- p.69 / Chapter 4.1.4 --- Curvature Estimation --- p.70 / Chapter 4.1.5 --- Multi-Dimensional Scaling (MDS) --- p.71 / Chapter 4.2 --- Result of the Core Matching Processes --- p.73 / Chapter 4.2.1 --- Posture Alignment Step --- p.73 / Chapter 4.2.2 --- Surface Fitting Step --- p.78 / Chapter 4.2.3 --- Feature Matching Refinement --- p.82 / Chapter 4.2.4 --- Application of the proposed algorithm --- p.84 / Chapter 4.2.4.1 --- Design Automation in Garment Industry --- p.84 / Chapter 4.3 --- Analysis --- p.86 / Chapter 4.3.1 --- Performance --- p.86 / Chapter 4.3.2 --- Accuracy --- p.87 / Chapter 4.3.3 --- Approach Comparison --- p.88 / Chapter Chapter 5 --- Conclusion --- p.89 / Chapter 5.1 --- Strength and contributions --- p.89 / Chapter 5.2 --- Limitation and future works --- p.90 / References --- p.91