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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.

This research focused on the off-line cursive script recognition application. The problem is very large and difficult and there is much room for improvement in every aspect of the problem. Many different aspects of this problem were explored in pursuit of solutions to create a more practical and usable off-line cursive script recognizer than is currently available.

視頻監控通常是指為了管理、引導和保護人們，通過電子設備監視和人們有關的行為、活動或者信息變化，例如使用閉路電視或者攔截遠距離電子傳輸的信息，如網絡流量，手機通信。視頻監控的潛在應用包括國土安全，反犯罪，交通控製，小孩、老人和病人的遠程看護。視頻監控技術為打擊恐怖主义和异常事件提供一小重要的防護。通過使用闭路电視摄像机等廉份的現代电子技朮使得視頻監控可成為可能。但是，除非一直監視著來自這些攝像機的反饋，否則它們提供僅僅是一些心理上安全。僱用足夠的人員來監督這些成千上萬的屏幕是不現實的，然而使用具有高度智能的現代自動化系統可以彌補這一空缺。 / 對于全天候地準確地管理成千上萬地攝像機，人工智能化的視頻監控是非常必要而且重要的。通常來說，智能監控包括以下部分: 1 信息獲取，如利用一個或者多個攝像機或者熱感成像或深度成像攝像機; 2 視頻分析，如目標檢測，識別，跟蹤，再識別或分割。3 存儲和傳輸，如編碼，分類和製片。在本文中，我們構建一個智能監控系統，其包括三個相互協作的摄像機用來估計感興趣物體的3D位置並且進行研究和跟蹤。為了識別物體，我們提出級聯頭肩檢測器尋找人臉區域進行識別。感興趣物體分割出來用于任意形狀物體編碼器對物體進行壓縮。 / 在第一部分中，我們討論如何使多個攝像頭在一起工作。在我們系統中，兩個固定的攝像機像人眼一樣註視著整個監控場景，搜尋非正常事件。如果有警報被非正常事件激活， PTZ攝像機會用來處理該事件，例如去跟蹤或者調查不明物體。利用相機標定技術，我們可以估計出物體的3D信息并將其傳輪到三個攝像機。 / 在第二部分中，我們提出級聯頭肩檢測器來檢測正面的頭肩并進行高級別的物體分析，例如識別和異常行為分析。在檢測器中，我們提出利用級聯結構融閤兩種強大的特徵， Harar-like 特微和HOG特徽，他們能有傚的檢測人臉和行人。利用Harr-like特徵，頭肩檢測器能夠在初期用有限的計算去除非頭肩區域。檢測的區域可以用來識別和分割。 / 在第三部分中，利用訓練的糢型，人臉區域可以從檢測到的頭肩區域中提取。利用CAMshift對人臉區域進行細化。在視頻監控的環境中，人臉識別是十分具有挑戰性的，因為人臉圖像受到多種因素的影響，例如在不均勻光綫條件下變化姿態和非聚焦糢糊的人臉。基于上述觀測，我們提出一種使用OLPF特微結閤AGMM糢型的人臉識別方法，其中OLPF特徵不僅不受糢糊圖像的影響，而且對人臉的姿態很魯棒。AGMM能夠很好地構建多種人臉。對標準測試集和實際數據的實驗結果證明了我們提出的方法一直地优于其它最先進的人臉識別方法。 / 在第四部分中，我們提出一種自動人體分割系統。首先，我們用檢測到的人臉或者人體對graph cut分割模型初始化并使用max-flow /min-cut算法對graph進行優化。針對有缺點的檢測目標的情況，采用一種基于coarse-to-fine的分割策略。我們提出抹除背景差別技術和自適應初始化level set 技術來解決存在于通用模型中的讓人頭疼的分割問題，例如發生在高差別的物體邊界區域或者在物體和背景中存在相同顏色的錯誤分割。實驗結果證明了我們的人體分割系統在實時視頻圖像和具有復雜背景的標準測試序列中都能很好的運作。 / 在最后部分中，我們專註于怎么樣對視頻內容進行智能的壓縮。在最近幾十年里，視頻編碼研究取得了巨大的成就，例如H.264/AVC標準和下一代的HEVC標準，它們的壓縮性能大大的超過以往的標準，高于50% 。但是相對于MPEG-4 ，在最新的編碼標準中缺少了壓縮任意形狀物體的能力。雖然在現在的H.264/AVC 中提供了片組結構和彈性模塊組閤技術，但是它仍然不能準確地高效地處理任意形狀區域。為了解決H.264/AVC 的這一缺點，我們提出基于H.264/AVC編碼框架的任意形狀物體編碼，它包括二值圖像編碼，運動補償和紋理編碼。在我們系統里，我們采用了1) 用新的運動估計改進的二值圖像編碼，它對二值塊的預測很有用。2) 在紋理編碼中，采用新的任意形狀整型變換來壓縮紋理信息，它是一種從4x4的ICT衍生出來的變換。3)和一些讓該編碼器勻新的框架兼容的相關編碼技術。我們把編碼器應用到高清視頻序列並且從客觀方便和主觀方面對編碼器進行評估。實驗結果證明了我們的編碼器遠遠超越以前的物體編碼方法並且十分接近H.264/AVC 的編碼性能。 / Surveillance is the process of monitoring the behaviour, activities, or changing information, usually of people for the purpose of managing, directing or protecting by means of electronic equipment, such as closed-circuit television (CCTV) camera or interception of electronically transmitted information from a distance, such as Internet or phone calls. Some potential surveillance applications are homeland security, anti-crime, traffic control, monitoring children, elderly and patients at a distance. Surveillance technology provides a shield against terrorism and abnormal event, and cheap modern electronics makes it possible to implement with CCTV cameras. But unless the feeds from those cameras are constantly monitored, they only provide an illusion of security. Finding enough observers to watch thousands of screens simply is impractical, yet modern automated systems can solve the problems with a surprising degree of intelligence. / Surveillance with intelligence is necessary and important to accurately mange the information from millions of sensors in 7/24 hours. Generally, intelligent surveillance includes: 1. information acquirement, like a single or the collaboration of multiple cameras, thermal or depth camera; 2. video analysis, like object detection, recognition, tracking, re-identification and segmentation; 3. storage and transmission, like coding, classification, and footage. In this thesis, we build an intelligent surveillance system, in which three cameras working collaboratively to estimate the position of the object of interest (OOI) in 3D space, investigate and track it. In order to identify the OOI, Cascade Head-Shoulder Detector is proposed to find the face region for recognition. The object can be segmented out and compressed by arbitrarily shaped object coding (ASOC). / In the first part, we discuss how to make the multiple cameras work together. In our system, two stationary cameras, like human eyes, are focusing on the whole scene of the surveillance region to observe abnormal events. If an alarm is triggered by abnormal instance, a PTZ camera will be assigned to deal with it, such as tracking orinvestigating the object. With calibrated cameras, the 3D information of the object can be estimated and communicated among the three cameras. / In the second part, cascade head-shoulder detector (CHSD) is proposed to detect the frontal head-shoulder region in the surveillance videos. The high-level object analysis will be performed on the detected region, e.g., recognition and abnormal behaviour analysis. In the detector, we propose a cascading structure that fuses the two powerful features: Haar-like feature and HOG feature, which have been used to detect face and pedestrian efficiently. With the Haar-like feature, CHSD can reject most of non-headshoulder regions in the earlier stages with limited computations. The detected region can be used for recognition and segmentation. / In the third part, the face region can be extracted from the detected head-shoulder region with training the body model. Continuously adaptive mean shift (CAMshift) is proposed to refine the face region. Face recognition is a very challenging problem in surveillance environment because the face image suffers from the concurrence of multiple factors, such as a variant pose with out-of-focused blurring under non-uniform lighting condition. Based on this observations, we propose a face recognition method using overlapping local phase feature (OLPF) feature and adaptive Gaussian mixture model (AGMM). OLPF feature is not only invariant to blurring but also robust to pose variations and AGMM can robustly model the various faces. Experiments conducted on standard dataset and real data demonstrate that the proposed method consistently outperforms the state-of-art face recognition methods. / In the forth part, we propose an automatic human body segmentation system. We first initialize graph cut using the detected face/body and optimize the graph by maxflow/ min-cut. And then a coarse-to-fine segmentation strategy is employed to deal with the imperfectly detected object. Background contrast removal (BCR) and selfadaptive initialization level set (SAILS) are proposed to solve the tough problems that exist in the general graph cut model, such as errors occurred at object boundary with high contrast and similar colors in the object and background. Experimental results demonstrate that our body segmentation system works very well in live videos and standard sequences with complex background. / In the last part, we concentrate on how to intelligently compress the video context. In recent decades, video coding research has achieved great progress, such as inH.264/AVC and next generation HEVC whose compression performance significantly exceeds previous standards by more than 50%. But as compared with the MPEG-4, the capability of coding arbitrarily shaped objects is absent from the following standards. Despite of the provision of slice group structures and flexible macroblock ordering (FMO) in the current H.264/AVC, it cannot deal with arbitrarily shaped regions accurately and efficiently. To solve the limitation of H.264/AVC, we propose the arbitrarily shaped object coding (ASOC) based on the framework H.264/AVC, which includes binary alpha coding, motion compensation and texture coding. In our ASOC, we adopt (1) an improved binary alpha Coding with a novel motion estimation to facilitate the binary alpha blocks prediction, (2) an arbitrarily shaped integer transform derivative from the 4×4 ICT in H.264/AVC to code texture and (3) associated coding techniques to make ASOC more compatible with the new framework. We extent ASOC to HD video and evaluate it objectively and subjectively. Experimental results prove that our ASOC significantly outperforms previous object-coding methods and performs close to the H.264/AVC. / 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. / Liu, Qiang. / "November 2012." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 123-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstracts in English and Chinese. / Dedication --- p.ii / Acknowledgments --- p.iii / Abstract --- p.vii / Publications --- p.x / Nomenclature --- p.xii / Contents --- p.xviii / List of Figures --- p.xxii / List of Tables --- p.xxiii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and objectives --- p.1 / Chapter 1.2 --- A brief review of camera calibration --- p.2 / Chapter 1.3 --- Object detection --- p.5 / Chapter 1.3.1 --- Face detection --- p.5 / Chapter 1.3.2 --- Pedestrian detection --- p.7 / Chapter 1.4 --- Recognition --- p.8 / Chapter 1.5 --- Segmentation --- p.10 / Chapter 1.5.1 --- Thresholding-based methods --- p.11 / Chapter 1.5.2 --- Clustering-based methods --- p.11 / Chapter 1.5.3 --- Histogram-based methods --- p.12 / Chapter 1.5.4 --- Region-growing methods --- p.12 / Chapter 1.5.5 --- Level set methods --- p.13 / Chapter 1.5.6 --- Graph cut methods --- p.13 / Chapter 1.5.7 --- Neural network-based methods --- p.14 / Chapter 1.6 --- Object-based video coding --- p.14 / Chapter 1.7 --- Organization of thesis --- p.16 / Chapter 2 --- Cameras Calibration --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Basic Equations --- p.21 / Chapter 2.2.1 --- Parameters of Camera Model --- p.22 / Chapter 2.2.2 --- Two-view homography induced by a Plane --- p.22 / Chapter 2.3 --- Pair-wise pose estimation --- p.23 / Chapter 2.3.1 --- Homography estimation --- p.24 / Chapter 2.3.2 --- Calculation of n and λ --- p.24 / Chapter 2.3.3 --- (R,t) Estimation --- p.25 / Chapter 2.4 --- Distortion analysis and correction --- p.27 / Chapter 2.5 --- Feature detection and matching --- p.28 / Chapter 2.6 --- 3D point estimation and evaluation --- p.30 / Chapter 2.7 --- Conclusion --- p.34 / Chapter 3 --- Cascade Head-Shoulder Detector --- p.35 / Chapter 3.1 --- Introduction --- p.35 / Chapter 3.2 --- Cascade head-shoulder detection --- p.36 / Chapter 3.2.1 --- Initial feature rejecter --- p.37 / Chapter 3.2.2 --- Haar-like rejecter --- p.39 / Chapter 3.2.3 --- HOG feature classifier --- p.40 / Chapter 3.2.4 --- Cascade of classifiers --- p.45 / Chapter 3.3 --- Experimental results and analysis --- p.46 / Chapter 3.3.1 --- CHSD training --- p.46 / Chapter 3.4 --- Conclusion --- p.49 / Chapter 4 --- A Robust Face Recognition in Surveillance --- p.50 / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Cascade head-shoulder detection --- p.53 / Chapter 4.2.1 --- Body model training --- p.53 / Chapter 4.2.2 --- Face region refinement --- p.54 / Chapter 4.3 --- Face recognition --- p.56 / Chapter 4.3.1 --- Overlapping local phase feature (OLPF) --- p.56 / Chapter 4.3.2 --- Fixed Gaussian Mixture Model (FGMM) --- p.59 / Chapter 4.3.3 --- Adaptive Gaussian mixture model --- p.61 / Chapter 4.4 --- Experimental verification --- p.62 / Chapter 4.4.1 --- Preprocessing --- p.62 / Chapter 4.4.2 --- Face recognition --- p.63 / Chapter 4.5 --- Conclusion --- p.66 / Chapter 5 --- Human Body Segmentation --- p.68 / Chapter 5.1 --- Introduction --- p.68 / Chapter 5.2 --- Proposed automatic human body segmentation system --- p.70 / Chapter 5.2.1 --- Automatic human body detection --- p.71 / Chapter 5.2.2 --- Object Segmentation --- p.73 / Chapter 5.2.3 --- Self-adaptive initialization level set --- p.79 / Chapter 5.2.4 --- Object Updating --- p.86 / Chapter 5.3 --- Experimental results --- p.87 / Chapter 5.3.1 --- Evaluation using real-time videos and standard sequences --- p.87 / Chapter 5.3.2 --- Comparison with Other Methods --- p.87 / Chapter 5.3.3 --- Computational complexity analysis --- p.91 / Chapter 5.3.4 --- Extensions --- p.93 / Chapter 5.4 --- Conclusion --- p.93 / Chapter 6 --- Arbitrarily Shaped Object Coding --- p.94 / Chapter 6.1 --- Introduction --- p.94 / Chapter 6.2 --- Arbitrarily shaped object coding --- p.97 / Chapter 6.2.1 --- Shape coding --- p.97 / Chapter 6.2.2 --- Lossy alpha coding --- p.99 / Chapter 6.2.3 --- Motion compensation --- p.102 / Chapter 6.2.4 --- Texture coding --- p.105 / Chapter 6.3 --- Performance evaluation --- p.108 / Chapter 6.3.1 --- Objective evaluations --- p.108 / Chapter 6.3.2 --- Extension on HD sequences --- p.112 / Chapter 6.3.3 --- Subjective evaluations --- p.115 / Chapter 6.4 --- Conclusions --- p.119 / Chapter 7 --- Conclusions and future work --- p.120 / Chapter 7.1 --- Contributions --- p.120 / Chapter 7.1.1 --- 3D object positioning --- p.120 / Chapter 7.1.2 --- Automatic human body detection --- p.120 / Chapter 7.1.3 --- Human face recognition --- p.121 / Chapter 7.1.4 --- Automatic human body segmentation --- p.121 / Chapter 7.1.5 --- Arbitrarily shaped object coding --- p.121 / Chapter 7.2 --- Future work --- p.122 / Bibliography --- p.123

This work is concerned with the problem of 3-D object recognition and orientation determination from single 2-D image frames in which objects may be noisy, partially occluded or both. Global descriptors of shape such as moments and Fourier descriptors rely on the whole shape being present. If part of a shape is missing then all of the descriptors will be affected. Consequently, such approaches are not suitable when objects are partially occluded, as results presented here show. Local methods of describing shape, where distortion of part of the object affects only the descriptors associated with that particular region, and nowhere else, are more likely to provide a successful solution to the problem. One such method is to locate points of maximum curvature on object boundaries. These are commonly believed to be the most perceptually significant points on digital curves. However, results presented in this thesis will show that estimators of point curvature become highly unreliable in the presence of noise. Rather than attempting to locate such high curvature points directly, an approach is presented which searches for boundary segments which exhibit significant linearity; curvature discontinuities are then assigned to the junctions between boundary segments. The resulting object descriptions are more stable in the presence of noise. Object orientation and recognition is achieved through a directed search and comparison to a database of similar 2-D model descriptions stored at various object orientations. Each comparison of sensed and model data is realised through a 2-D pose-clustering procedure, solving for the coordinate transformation which maps model features onto image features. Object features are used both to control the amount of computation and to direct the search of the database. In conditions of noise and occlusion objects can be recognised and their orientation determined to within less than 7 degrees of arc, on average.

人體姿態估計系統是用於從視頻圖像中判斷人體在空間中姿態的系統。該系統面臨的主要的問題有：人體姿態空間的維度高；人體四肢的深度信息不確定；人體可以穿多種衣服；人體經常會被自身遮擋。多攝像頭系統可以觀察到人體同一姿態的更多數據，因此可以有效的克服人體姿態估計的不確定性。在本研究中，我們採用多種方法研究多攝像頭人體姿態估計系統，並提出了一種融合多種約束的框架。 / 在多攝像頭系統中，可以用到的約束包括：（一）圖像觀測約束：估計的人體姿態投影到圖像中需要和所有視角的觀察一致，（二）人體姿態可行性約束：人體部位之間要滿足身體連接約束並且估計所得的人體姿態要符合真實人體的要求，（三）三維剛體約束：從不同視角觀察到的人體要保持空間一致性，（四）行為約束：人體的姿態應與先驗的行為信息保持一致。本研究的目標是開發出一個可以同時利用上述約束的多攝像頭系統，該系統將可以同時無縫的整合多個攝像頭，並且可以穩定有效的估計人體的三維姿態。本文研究了基於單目系統的三維人體姿態估計方法，並基於約束一和約束二提出了一個新的人體模型估計人體姿態；本文提出仿射立體投影模型，並將該模型用於整合多個視角的觀察數據，從而使姿態估計同時得到約束一，約束二和約束三的支持；本文展示了如何使用多視角行為流形庫同時應用以上提到的四種約束，並有效的估計三維人體姿態；最後我們提出了基於流形庫的部分輸入高斯過程處理人體姿態估計腫的遮擋問題。 / 本論文有以下貢獻：（1）首次提出了仿射立體投影模型並將其用於描述三維剛體約束。使用這種方法，可以方便的將三維剛體約束集成於由底向上的人體姿態估計框架。（2）將人體姿態可行性約束以及三維剛體約束同時集成於多視角流型庫。即使在多行為的環境中，該方法也可以直接把多視角觀察數據映射至人體姿態空間。（3）通過綜合分析多個視角的數據，該系統可以有效的克服自我遮擋問題。（4）該系統易於擴展，基於仿射立體投影模型的方法和基於多視角流形庫的方法都可以用在多於三個攝像頭的系統中。 / A human pose estimation system is to determine the full human body pose in space from merely video data. Key difficulties of this problem include: full body kinematics is of high dimensionality, limb depths are ambiguous, people can impose various clothes, and there are often self-occlusions. The use of multiple views could enhance robustness of the solution toward uncertainties, as more data are collected about the same pose. In this research, we study multi-view based human pose estimation by exploring a variety of approaches and propose a framework that integrates multiple constraints. / In a multiple view system, the constraints that could be applied for human pose estimation include: (1) Image evidence: the projection of the estimated 3D human body should satisfy the 2D observations in all views, (2) Feasible human pose: neighboring body parts should be connected according to the body articulation and all joints angles should stay feasible, (3) 3D object rigidity: the corresponding parts over all views should satisfy the multi-view consistency, and (4) Action context: the detected results should be in line with prior knowledge about the possible “activities“. The objective of this research is to develop a multiple view system that could embed all the above constraints in a natural way while integrate more cameras into the system seamlessly to enhance robustness. Specifically, we investigate the part based monocular 3D estimation algorithm and develop a novel human model to assist the pose inference based on the constraint (1) and (2); we propose an affine stereo model to associate multiple views’ data so that body pose inference is supported by constraint (1), (2) and (3) simultaneously; we present how to apply multi-view activity manifold library to associate multiple views and estimate human pose in 3D efficiently so that all the four constraints are integrated into one framework; and we finally propose a partial-input Gaussian process to handle the body occlusion problem within the manifold library framework. / The thesis has four contributions: (1), an affine stereo approach is developed to efficiently explore the object rigidity, and this constraint is integrated into a bottom-up framework smoothly. (2), a multi-view visual manifold library is proposed to capture the human body articulation and rigidity in the multi-activity context, simplifying the pose estimation into a direct mapping from multi-view image evidence to 3D pose. (3), the multi-view system efficiently solves the self-occlusion problem by analyzing multi-view’s data. (4), the multi-view system is designed to be scalable; both the affine stereo based approach and the multi-view visual manifold library based approach could be applied to systems with more than 3 cameras. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Zibin. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 144-150). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / ABSTRACT --- p.i / 摘要 --- p.iii / ACKNOWLEDGEMENTS --- p.v / TABLE OF CONTENTS --- p.vi / LIST OF FIGURES --- p.xi / LIST OF TABLES --- p.xviii / Chapter Chapter One : --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Goals --- p.3 / Chapter 1.3 --- Challenges --- p.3 / Chapter 1.3.1 --- High Dimensional State Space --- p.3 / Chapter 1.3.2 --- Observations --- p.4 / Chapter 1.3.3 --- Multiple Views Integration --- p.6 / Chapter 1.4 --- Summary of the Approach --- p.7 / Chapter 1.5 --- Thesis Overview --- p.9 / Chapter Chapter Two : --- Background --- p.10 / Chapter 2.1 --- Top-down Framework --- p.11 / Chapter 2.1.1 --- Background Subtraction --- p.11 / Chapter 2.1.2 --- Deterministic Approach --- p.13 / Chapter 2.1.3 --- Sampling based Approach --- p.14 / Chapter 2.1.4 --- Regression based Method --- p.16 / Chapter 2.2 --- Bottom-up Framework --- p.17 / Chapter 2.2.1 --- Efficient Pictorial Structure --- p.18 / Chapter 2.2.2 --- Discriminative Part Detector --- p.19 / Chapter 2.2.3 --- Sampling based Inference --- p.20 / Chapter 2.2.4 --- Temporal Information --- p.21 / Chapter 2.3 --- Human Pose Estimation using Range Sensor --- p.21 / Chapter 2.4 --- Conclusion --- p.22 / Chapter Chapter Three : --- Pose Estimation from Single View --- p.23 / Chapter 3.1 --- Related Works --- p.25 / Chapter 3.2 --- The 3D Human Model --- p.26 / Chapter 3.3 --- Acquiring the Appearance Facet --- p.29 / Chapter 3.3.1 --- 2D Appearance Extraction from Each Training Image --- p.30 / Chapter 3.3.2 --- Acquiring 3D Appearance --- p.31 / Chapter 3.4 --- Data Driven Belief Propagation for Pose Estimation --- p.32 / Chapter 3.4.1 --- A Bayesian Formulation --- p.32 / Chapter 3.4.2 --- Belief Propagation --- p.34 / Chapter 3.4.3 --- Importance Function Sampling --- p.37 / Chapter 3.5 --- Experimental Results --- p.40 / Chapter 3.6 --- Conclusion --- p.45 / Chapter Chapter Four : --- Integrating Multiple Views using Affine Stereo Model --- p.46 / Chapter 4.1 --- Related Works --- p.48 / Chapter 4.2 --- Human Model and Problem Formulation --- p.50 / Chapter 4.3 --- Associating Multiple Image Streams --- p.53 / Chapter 4.3.1 --- Linear Relation of Multiple Views --- p.54 / Chapter 4.3.2 --- Rank Constraint --- p.58 / Chapter 4.4 --- Human Pose Estimation System using Multi-view and Other Constraints --- p.62 / Chapter 4.4.1 --- Body Part Candidates from Discriminative Body Part Detector --- p.63 / Chapter 4.4.2 --- From Body Part Candidates to Body Candidates in each view --- p.65 / Chapter 4.4.3 --- Associating Body Candidates across Views --- p.67 / Chapter 4.5 --- Experimental Results --- p.74 / Chapter 4.5.1 --- Evaluation of the Multi-view Linear Relationship --- p.74 / Chapter 4.5.2 --- Performance over the HumanEva Dataset --- p.79 / Chapter 4.6 --- Conclusion --- p.86 / Chapter Chapter Five : --- Integrating Multiple Views using Activity Manifold Library --- p.88 / Chapter 5.1 --- Related Works --- p.90 / Chapter 5.2 --- Multi-view Manifold Library --- p.93 / Chapter 5.2.1 --- Body Representation in Space and Views --- p.94 / Chapter 5.2.2 --- Human-orientation-dependent Multi-view Visual Manifold --- p.95 / Chapter 5.3 --- Human Pose Estimation in 3D via Multi-view Manifold --- p.97 / Chapter 5.3.1 --- Find Multi-view Body Hypothesis in 2D --- p.97 / Chapter 5.3.2 --- Mutual Selection between Multi-view Body Hypothesises and Manifolds --- p.99 / Chapter 5.4 --- Experimental Results --- p.102 / Chapter 5.4.1 --- Synthetic Data Test --- p.103 / Chapter 5.4.2 --- Real Image Evaluation --- p.108 / Chapter 5.4.3 --- Qualitative Test for Generalization Capability --- p.110 / Chapter 5.4.4 --- Calculation Speed --- p.114 / Chapter 5.5 --- Conclusion --- p.115 / Chapter Chapter Six : --- Partial-Input Gaussian Process for Inferring Occluded Human Pose --- p.116 / Chapter 6.1 --- Related Works --- p.118 / Chapter 6.2 --- Human-orientation-invariant Multi-view Visual Manifold --- p.119 / Chapter 6.3 --- Human Pose estimation in 3D via Multi-view Manifold --- p.121 / Chapter 6.3.1 --- 2D Pre-processing --- p.121 / Chapter 6.3.2 --- Mutual Selection between Multi-view Body Hypothesises and Manifolds --- p.121 / Chapter 6.3.3 --- Occlusion Detection and Partial-input Gaussian Process --- p.122 / Chapter 6.4 --- Experimental Results --- p.126 / Chapter 6.4.1 --- Multi-view Manifolds and Evaluations for Different Views --- p.126 / Chapter 6.4.2 --- Evaluation for Occlusion Data --- p.131 / Chapter 6.4.3 --- Evaluation for Gavrila’s Dataset --- p.132 / Chapter 6.4.4 --- Qualitative Test for Generalization Capability --- p.134 / Chapter 6.5 --- Conclusion --- p.139 / Chapter Chapter Seven : --- Conclusions and Future Works --- p.140 / Chapter 7.1 --- Conclusion --- p.140 / Chapter 7.2 --- Limitation --- p.142 / Chapter 7.3 --- Future Directions --- p.142 / Bibliography --- p.144

Hung, Chun Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 58-63). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Generalized Photometric Stereo --- p.6 / Chapter 2.1 --- Problem Description --- p.6 / Chapter 2.2 --- Related Work --- p.9 / Chapter 2.3 --- Photometric Stereo with Environment Lighting --- p.11 / Chapter 2.4 --- Estimating Surface Normals --- p.13 / Chapter 2.4.1 --- Surface Normal and Albedo Estimation --- p.14 / Chapter 2.5 --- Data Acquisition Configuration --- p.17 / Chapter 2.6 --- Issues --- p.19 / Chapter 2.7 --- Outlier Removal --- p.22 / Chapter 2.8 --- Experimental Results --- p.23 / Chapter 3 --- Generalized Stereo Matching --- p.30 / Chapter 3.1 --- Problem Description --- p.30 / Chapter 3.2 --- Related Work --- p.32 / Chapter 3.3 --- Our Approach --- p.33 / Chapter 3.3.1 --- Notations and Problem Introduction --- p.33 / Chapter 3.3.2 --- Depth and Motion Initialization --- p.35 / Chapter 3.3.3 --- Volume-based Structure Prior --- p.38 / Chapter 3.3.4 --- Objective Function with Volume-based Priors --- p.43 / Chapter 3.3.5 --- Numerical Solution --- p.46 / Chapter 3.4 --- Results --- p.48 / Chapter 4 --- Conclusion --- p.56 / Bibliography --- p.57

This thesis explores moving information processing by means of quantum computation technology via neural networks. A new quantum computation algorithm achieves a double-accurate outcome on measuring optical flows in a video. A set of neural networks act as experimental tools that manipulate the applied data. Attempts have been made to calculate a pixel's location, velocity and grey scale value of moving images but the location and velocity could not be simultaneously measured precisely enough in accordance with both classical and quantum uncertainty principles. The error in measurement produced by quantum principles was found to be half that produced by a classical approach. In some circumstances the ratio of a pixel's coordinates and that of velocities could be determined using quantum eigenstate theory. The Hamiltonian of interaction of two NOT gates is most likely to represent the Gibbs potential distribution in calculating the posterior probability of an image. A quantum chain code algorithm was erected to describe the edges of image features. The FACEFLOW experimental system was built in order to classify the moving human faces. Three kinds of neural network models were finally presented. / Doctor of Philosophy (PhD)

<p>The communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real- time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.</p>

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xvi, 131 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Tamal K. Dey, Dept. of Computer and Information Science. Includes bibliographical references (p. 126-131).