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投影機相機(projector camera)系統近年相當流行，主要原因是它能夠靈活地展示影像，使用戶有更大的自由度作出操作。手提式投影機的技術在過往幾年急速發展、漸見成熟，知名的家用電子産品生産廠閱始推出内置迷你投影機的手機和攝影機。另一方面手機的運算能力正急劇地提升，它們多都配置不同種類且功能强大的周邊設備。 / 本論文提出並討論一種基於現場可编程邏輯閘陣列(Field Programmable Gate Array, FPGA),並適用於嵌入式系统的特殊處理器。該特殊處理器專門處理來自相機的資料串流，透過一系列的象素圖像處理運算如圖像梯度和高斯模糊，去找出相中物件的邊緣，藉此分擔微處器在運算上的負擔。實驗結果明這特殊處理器可實現於低端的FPGA上並和普遍的微處器一起運作。 / 本論文第二個探討的主題是一個利用多模卡爾曼濾波器(Multiple Model Kalman Filter)的直線追踪器，並利用多個直線追踪器去作投影面板的追踪。利用卡爾曼濾波器只需要很低的運算能力的優點,我們的直線追踪器在嵌入式系统實測時能達到每秒200幀的速度。多模卡爾曼濾波器在實驗中有滿意的成績並較單卡爾曼濾波器和擴展卡爾曼濾波器優異。 / Projector-camera (ProCam in short) systems are getting very popular since the user can change the display area dynamically and enjoy more freedom in handling the device. In recent years, the mobile projector technology is becoming mature and manufacturers are shipping mobile phones and digital cameras with projectors. On the other hand, the computation power of a cell phone had dramatically increased and the cell phones are accompanied with large number of powerful peripherals. / In this thesis, the possibility of making an embedded Projector-camera (ProCam) system is investigated. A ProCam system is developed by our research group previously and designed for desktop Personal Computers(PCs). The system uses computer vision techniques to detect a white cardboard as the projection screen and uses particle filter to trace the screen in subsequent frames. The system demands a large computation power, unfortunately the power of low cost embedded system is still not powerful enough to implement the ProCam system.Therefore, specially designed hardware and computationally efficient algorithm are required in order to implement the ProCam system on an embedded system. / An FPGA based special processor to share the workload of the microcontroller in the embedded system is proposed and tested. This special processor will take the data stream of the camera as the inputs and apply pixel-wise image operators such as image gradient and Gaussian blur in order to extract the edge pixels. As a result, the workload of the microcontroller in the embedded system is reduced. The experiments show that the design can be implement on a low-end FPGA with a simple microcontroller. / A line tracker using Multiple Model Kalman lter is also proposed in this thesis. The aim of this tracker is to reduce the time on tracking the board. Benet from the low computation requirement of Kalman filter, the proposed line tracker can run in 200 fps on our testing embedded system. The experiments also show that the robustness of the Multiple Model Kalman filter is satisfactory and it outperforms the line trackers using single Kalman filter or extended Kalman filter alone. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Fung, Hung Kwan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 115-124). / Abstracts also in Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contributions --- p.3 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Projector-Camera System --- p.8 / Chapter 2.2.1 --- Static Projector-Screen --- p.9 / Chapter 2.2.2 --- Dynamic Projector-Screen --- p.13 / Chapter 2.3 --- Embedded Vision --- p.15 / Chapter 2.4 --- Summary --- p.25 / Chapter 3 --- System Overview --- p.26 / Chapter 3.1 --- System Design --- p.26 / Chapter 3.2 --- Our Approach --- p.28 / Chapter 3.2.1 --- Projector-camera system --- p.28 / Chapter 3.2.2 --- Smart Camera --- p.31 / Chapter 3.2.3 --- Quadrangle Detection and Tracking Module --- p.32 / Chapter 3.2.4 --- Projection Module --- p.32 / Chapter 3.3 --- Extension --- p.33 / Chapter 4 --- Smart Camera --- p.34 / Chapter 4.1 --- Introduction --- p.34 / Chapter 4.2 --- Hardware Overview --- p.35 / Chapter 4.3 --- Image Acquisition --- p.40 / Chapter 4.4 --- Image Processing --- p.42 / Chapter 4.4.1 --- RGB-to-Gray Conversion Module . --- p.44 / Chapter 4.4.2 --- Image Smoothing Module --- p.45 / Chapter 4.4.3 --- Image Gradient Module --- p.49 / Chapter 4.4.4 --- Non-maximum Suppression and Hysteresis Thresholding --- p.53 / Chapter 4.5 --- Summary --- p.55 / Chapter 5 --- Quadrangle Detection and Tracking --- p.57 / Chapter 5.1 --- Introduction --- p.57 / Chapter 5.2 --- Line Feature Extraction --- p.61 / Chapter 5.3 --- Automatic Quadrangle Detection --- p.62 / Chapter 5.4 --- Real-time Quadrangle Tracking --- p.68 / Chapter 5.4.1 --- Line Tracker --- p.69 / Chapter 5.5 --- Tracking Lose Strategy --- p.76 / Chapter 5.6 --- Recover from Tracking Failure --- p.77 / Chapter 5.7 --- Summary --- p.77 / Chapter 6 --- Implementation and Experiment Result --- p.79 / Chapter 6.1 --- Introduction --- p.79 / Chapter 6.2 --- Smart Camera --- p.79 / Chapter 6.3 --- Line Tracking --- p.87 / Chapter 7 --- Limitation and Discussion --- p.101 / Chapter 7.1 --- Introduction --- p.101 / Chapter 7.2 --- Limitation --- p.101 / Chapter 7.3 --- Summary --- p.105 / Chapter 8 --- Application --- p.107 / Chapter 8.1 --- Introduction --- p.107 / Chapter 8.2 --- Portable Projector-Camera System --- p.107 / Chapter 8.3 --- Summary --- p.110 / Chapter 9 --- Conclusion --- p.112 / Bibliography --- p.115

Heavy vehicle weights must be closely monitored to prevent fatigue-induced deterioration and critical fracture to civil infrastructure, among many other purposes. Developing a cost-effective weigh-in-motion (WIM) system remains challenging. This doctoral research describes the creation and experimental validations of a computer vision-based non-contact vehicle WIM system. The underlining physics is that the force exerted by each tire onto the roadway is the product of the two key vehicle parameters: tire-roadway contact pressure and contact area. Computer vision is applied (1) to measure the several tire parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire pneumatic pressure can be found. Consequently, a computer vision system is developed in this research. The computer vision system comprises a camera and computer vision software/techniques for measuring the tire parameters and recognizing the tire sidewall markings from individual tire images of a moving vehicle. Computer vision techniques, such as edge detection and optical character recognition (OCR), are applied to enhance the measurements and recognition accuracy. Numerous laboratory and field experiments were conducted on a sport utility vehicle and fully loaded or empty concrete trucks to demonstrate the feasibility of this novel method. The vehicle weights estimated by this novel computer vision-based non-contact vehicle WIM system agreed well with the curb weights verified by static weighing, demonstrating the potential of this computer vision-based method as a non-contact means for weighing vehicles in motion. To further illustrate and exemplify the versatility of this novel computer vision-based WIM system, this research explores the potential application capability of the system for structural health monitoring (SHM) in civil engineering. This work aims to investigate the potential of this proposed and prototyped computer vision-based vehicle WIM system to acquire vehicle weight and location information as well as to obtain corresponding bridge responses simultaneously for later structural model updating analysis and damage detection and identification framework. In order to validate the concept, a laboratory vehicle-bridge model was constructed. Subsequently, numerous experiments were carried out to demonstrate how the computer vision-based WIM system can be utilized as a resourceful application to (1) extract bridge responses, (2) estimate vehicle weight, and (3) localize the input force simultaneously. This doctoral research delivers a unique, pioneering, and innovative design and development of a computer vision-based non-contact vehicle WIM method and system that can remotely perform vehicle weight estimation. It also demonstrates a novel application of computer vision technology to solve challenging weigh-in-motion (WIM) and civil engineering problems.

Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Industrial Engineering, Department of Industrial Engineering, Durban University of Technology, 2009. / This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

Gong, Yibo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 99-108). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.2.1 --- Embedded fire detection platform --- p.4 / Chapter 1.2.2 --- Extended CAMSHIFT object detection frame work --- p.5 / Chapter 1.2.3 --- Cooperative multiple camera module --- p.8 / Chapter 1.2.4 --- Aerial maritime survivor detection system --- p.9 / Chapter 1.3 --- Organization of this thesis --- p.9 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Embedded computer vision --- p.11 / Chapter 2.2 --- Visual Fire detection --- p.12 / Chapter 2.3 --- Color-based object detection and tracking --- p.15 / Chapter 2.4 --- Multiple-camera system cooperation --- p.16 / Chapter 2.5 --- Multiple-camera system calibration --- p.18 / Chapter 3 --- Overview of the embedded fire detection system --- p.22 / Chapter 3.1 --- Functional modules of the detection unit --- p.25 / Chapter 3.2 --- Dataflow within the detection unit --- p.28 / Chapter 4 --- Simulated annealing based MEAN SHIFT framework --- p.31 / Chapter 4.1 --- Simulated annealing framework --- p.33 / Chapter 4.2 --- Combination of simulated annealing with MEAN SHIFT --- p.37 / Chapter 5 --- Extended CAMSHIFT framework for fire detection --- p.42 / Chapter 5.1 --- Bidirectional color histogram training and backprojection --- p.43 / Chapter 5.2 --- Choice of properly sized fire window --- p.48 / Chapter 5.3 --- Alternative optimization based search window resizing --- p.49 / Chapter 5.4 --- Multiple modal particle filter based window size optimization --- p.53 / Chapter 5.4.1 --- Multiple modal particle filter --- p.53 / Chapter 5.4.2 --- Integration of the MMPF with CAMSHIFT framework --- p.57 / Chapter 5.5 --- fire monitoring --- p.63 / Chapter 6 --- The multiple camera module --- p.65 / Chapter 6.1 --- Calibration of the multi-camera system --- p.66 / Chapter 6.2 --- Region mapping and cooperation among the cameras --- p.69 / Chapter 7 --- Implementation and Experiments --- p.71 / Chapter 7.1 --- Implementation --- p.71 / Chapter 7.2 --- Experiments and performance evaluations --- p.74 / Chapter 7.2.1 --- Bidirectional histogram training and backprojection --- p.76 / Chapter 7.2.2 --- Performance of the hybrid Simulated annealing-Mean shift framework --- p.78 / Chapter 7.2.3 --- Alternative optimization based search window resizing for CAMSHIFT --- p.84 / Chapter 7.2.4 --- Multiple modal particle filter based search window resizing for CAMSHIFT --- p.87 / Chapter 7.2.5 --- Real-scenario test on the arm system --- p.94 / Chapter 7.2.6 --- Comparison of the two search window resizing mechanisms --- p.96 / Chapter 7.2.7 --- Accuracy of the multiple camera calibration method --- p.97 / Chapter 8 --- Extension to aerial maritime survivor search --- p.99 / Chapter 8.1 --- Introduction --- p.99 / Chapter 8.2 --- Implementation and experiment results --- p.102 / Chapter 9 --- Conclusion --- p.105 / Chapter 9.1 --- Contribution and summary of the work --- p.105 / Chapter 9.2 --- Future work --- p.107 / Bibliography --- p.109

Thesis submitted in compliance with the requirements for the Master's Degree in Technology: Industrial Engineering, Department of Industrial Engineering, Durban University of Technology, 2009. / This research focuses on the design and implementation of an intelligent machine vision and sorting system that can be used to sort objects in an industrial environment. Machine vision systems used for sorting are either geometry driven or are based on the textural components of an object’s image. The vision system proposed in this research is based on the textural analysis of pixel content and uses an artificial neural network to perform the recognition task. The neural network has been chosen over other methods such as fuzzy logic and support vector machines because of its relative simplicity. A Bluetooth communication link facilitates the communication between the main computer housing the intelligent recognition system and the remote robot control computer located in a plant environment. Digital images of the workpiece are first compressed before the feature vectors are extracted using principal component analysis. The compressed data containing the feature vectors is transmitted via the Bluetooth channel to the remote control computer for recognition by the neural network. The network performs the recognition function and transmits a control signal to the robot control computer which guides the robot arm to place the object in an allocated position. The performance of the proposed intelligent vision and sorting system is tested under different conditions and the most attractive aspect of the design is its simplicity. The ability of the system to remain relatively immune to noise, its capacity to generalize and its fault tolerance when faced with missing data made the neural network an attractive option over fuzzy logic and support vector machines.

M.A. / This dissertation comprises an in-depth investigation into the domain of computer vision, with specific reference to the textile industry. The study consists of three main sections. In the first section, the computer-vision process is scrutinised in its entirety. Attention is given in this regard to what computer vision is, where it originated from, how it compares with human vision and what the motivations are for its implementation. Following, the computer-vision process is divided into four main components, namely image acquisition, image processing, image analysis and image interpretation. Subsequently, each component is discussed in greater detail, as well as aspects such as the hardware used in the course of the process, the algorithms that are implemented and the specific applications used for the process of analysis. In the second section, the focus is shifted to the textile industry, where our main focus lies. In this regard, examples are examined of the successful implementation of computer-vision technologies in the textile industry. In the third section, an investigation is launched into the specific problem for which a solution needs to be found in the present study, namely to determine whether computer vision constitutes a cost-effective way in which to locate broken thread during the spinning process. A wide range of algorithms has been applied for this purpose, whereafter the results of these experiments are announced.

Oyster meats are currently sorted by hand using volume as the sorting parameter. Hand grading is inaccurate, time consuming and costly. Previous research on physical properties of oyster meats showed a high correlation between projected area of oyster meats and their volume thus allowing the use of projected area measurements as a sorting criterion. A machine vision based oyster meat sorting machine was developed to mechanize the sorting process. The machine consists of a dark conveyor belt transporting singulated oysters through a grading station and then along a row of fast acting water jet valves which separates the stream of oysters into 3 classes. The vision system consists of a monochrome television camera, flash light illumination to "freeze" the images, a digitizer/transmitter and a Personal Computer as an image processing unit. Software synchronizes the flash light and digitization of images and calculates projected area of each meat using the planimeter method. The grading results are sent to a valve control board which actuates the spray valves. The sorting rate is 37 oyster meats/min with a sorting accuracy of 87.5%. A description of the design work, adjustment and l calibration procedures and a final sorting test is included. / Master of Science

Orientador: Paulo Roberto Gardel Kurka / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-21T02:23:47Z (GMT). No. of bitstreams: 1 MingotoJunior_CarlosRoberto_D.pdf: 4676498 bytes, checksum: f396cb633ba04f6ff1589cea747bb133 (MD5) Previous issue date: 2012 / Resumo: O presente projeto de pesquisa aplica técnicas de visão estereoscópica computacional no desenvolvimento da configuração de um equipamento de medição de ângulos de alinhamento de suspensão veicular, usando câmeras de vídeo de baixo custo. Atualmente, a maioria dos dispositivos de medição de ângulos de alinhamento de suspensão de veículos baseia-se no uso de componentes eletromecânicos, como pêndulos resistivos, inclinômetros capacitivos, dispositivos opto-mecânicos (espelhos e raio de luz monocromática de baixa intensidade). Com a sequência aqui estabelecida dos fundamentos algébricos e técnicas de visão computacional, realizam-se estudos de viabilidade científica e proposta de construção de um equipamento de verificação de ângulos de alinhamento veicular. São apresentados testes virtuais e reais, ilustrativos da potencialidade operacional do equipamento / Abstract: This research project uses stereoscopic computer vision techniques to develop a system to measure alignment angles of vehicular suspensions, using low cost cameras. Currently, most of the devices intended to measure vehicular suspension angles are based on the use of electromechanical components, such as resistive pendulums, capacitive inclinometers or opticmechanical devices (mirrors and projection of beams of monochromatic light of low intensity). Fundaments of linear algebra and computer vision techniques, lead to studies of feasibility and practical implementation of a system used to measure vehicular suspension alignment angles. Virtual and real measurements are carried out to illustrate the operative potential of such a system / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica