Within the timber industry the processing of sawn wood boards must be done in the right way to ensure that the product fulfills the requirements. Correct processing is crucial for wooden structures consisting of these will live up to their expectations. How the mounting of the board in wooden structures is performed to obtain satisfactory results is depending on how it is physically cupped. Due to this a curvature measuring device is used in the timber industry to detect how a sawn board is physically cupped. After detection, a proper processing can be performed to give a satisfactory product. The Swedish company Nolyx AB currently uses a curvature measuring device consisting of a smart camera with the task of taking a digital picture of the board end grain to determine its cupping. The smart camera currently has deficiencies that this work will investigate. The deficiencies are that the smart camera’s processing of images with certain properties do not give satisfactory results. The algorithms lack the robustness needed to cope with the variations of the item that might arise in the process. The desire of this study is that the smart cameras correctness in terms of variations in the object will increase, leading to financial gains for the company Nolyx AB and increased utilization of raw material for their customers. The result of this work is an algorithm that incrementally extracts and identifies the growth rings in the end grain of the board. The correctness of the image processing in this study is 82%, which is 22% higher compared to the smart camera. / Inom träindustrin måste bearbetning av sågade träbrädor ske på rätt sätt för att produkten skall uppfylla kraven. En korrekt bearbetning är avgörande för att träkonstruktioner bestående av dessa ska leva upp till sina förväntningar. Hur monteringen av brädan vid byggnation av träkonstruktioner utförs för att erhålla tillfredställande resultat beror på hur den fysiskt är kupad. På grund av detta används kupmätare inom träindustrin för att detektera brädans fysiska kupning. Efter detektering kan en korrekt bearbetning utföras vilket ger en tillfredsställande produkt. Företaget Nolyx AB använder idag en kupmätare som består av en smartkamera vars uppgift är att ta en digital bild av brädans ändträ för att avgöra dess kupning. Kupmätaren har idag brister som detta arbete skall angripa. Nämligen att smartkamerans behandling av bilder med vissa egenskaper inte ger tillfredsställande resultat. Algoritmerna saknar den robusthet som krävs för att klara de variationer på objektet som kan uppkomma i processen. Önskan med denna studie är att smartkamerans felfrihet vad gäller variationer i objektet ska öka, vilket leder till ekonomiska vinster för företaget Nolyx AB och ökat utnyttjande av råvaran för sina kunder. Resultatet av det här arbetet är en algoritm som stegvis extraherar och identifierar årsringarnas struktur. Felfriheten för bildbehandlingen i denna studie är 82 % vilket är 22 % högre jämfört med smartkameran.

A detecção de bordas é um primeiro passo importante no processamento digital de imagens, pois permite separar blocos distinto presentes em uma imagem. O desenvolvimento de sistemas autônomos que realizem tarefas a partir de informações visuais necessitam que o processamento destas seja realizado em tempo real. Este trabalho descreve a implementação de um processador, baseado numa arquitetura pipeline e no operador de máscara chamado Cruz de Roberts, dedicado para a extração de bordas em tempo real de imagens de vídeo. Tanto a entrada como a saída dos dados são em formato de vídeo composto monocromático padrão e foram utilizados circuitos digitais discretos para a implementação do processador. Os resultados são apresentados em forma de imagens e estas são comparadas com os resultados obtidos através de programas que realizam a detecção de bordas. / Edge detection is the first important step in digital image processing which allows to separate the distinct blocks present in an image. The development of automatic systemswhich perform operations from visual information need real time processing. This work describes the implementation of a pipeline processor based in the Robert\'s Cross mask operator, dedicated to extract edges from video images in real time. Both input and output video signals are monochromatic. Common digital circuits have been used to implement the processor. The results obtained are presented as images and are compared with edge detected images obtained from comercial software.

Ces travaux de recherche visaient à développer un système d'aide à la mobilité pour les personnes ayant une déficience visuelle (VIP ‘Visually Impaired Person’) appelé ‘Smart Environment Explorer Stick (SEES)’. Le but particulier de cette recherche était de développer de nouveaux algorithmes pour améliorer la précision de la détection de la présence de poteaux de la canne SEE-stick en utilisant la méthode de calcul de distance et la recherche de paires de lignes verticales basées sur l'optimisation de la technique de détection de contour de Canny. Désormais, l'algorithme de détection des poteaux est appelé l’algorithme YuRHoS. Le SEES développé comme système de support d'aide à la mobilité VIP a été intégré avec succès à plusieurs dispositifs tels que le serveur distant dénommé iSEE, le serveur local embarqué dénommé SEE-phone et la canne intelligente dénommée SEE-stick. Les performances de SEE-stick ont été améliorées grâce à l'algorithme YuRHoS qui permet de discriminer avec précision les objets (obstacles) en forme de poteau parmi les objets détectés. La comparaison des résultats de détection des poteaux avec ceux des autres algorithmes a conclu que l'algorithme YuRHoS était plus efficace et précis. Le lieu et la couleur des poteaux de test d’évaluation étaient deux des facteurs les plus importants qui influaient sur la capacité du SEE-stick à détecter leur présence. Le niveau de précision de SEE-stick est optimal lorsque le test d’évaluation est effectué à l'extérieur et que les poteaux sont de couleur argentée. Les statistiques montrent que la performance de l'algorithme YuRHoS à l'intérieur était 0,085 fois moins bonne qu'à l'extérieur. De plus, la détection de la présence de poteaux de couleur argentée est 11 fois meilleure que celle de poteaux de couleur noir. / This research aimed to develop a technology system of mobility aid for Visually Impaired Person (VIP) called Smart Environment Explorer Stick (SEES).Particular purpose of this research was developing new algorithm in improving accuracy of SEE-stick for pole detection using distance calculation method and vertical line pair search based on Canny edge detection optimization and Hough transform. Henceforth, the pole detection algorithm was named as YuRHoS algorithm.The developed SEES as supporting system of VIP mobility aid had been successfully integrated several devices such as global remote server (iSEE), embedded local server (SEE-phone) and smart stick (SEE-stick). Performance of SEE-stick could be improved through YuRHoS algorithm, which was able to fix the accuracy of SEE-stick in detecting pole. Test comparison of pole detection results among others algorithm concluded that YuRHoS algorithm had better accuracy in pole detection.Two most significant factors affecting SEE-stick ability in detecting pole was test location and pole color. Level of accuracy of SEE-stick would be optimum once the test location was performed outdoor and pole color was silver. Statistics result shown that YuRHoS algorithm performance indoor was 0.085 times worse than outdoor. Meanwhile, silver-pole-color as object detection could increase YuRHoS algorithm performance as much as 11 times better compare to black-pole-color.

Ocular fundus image can provide information on pathological changes caused by local ocular diseases and early signs of certain systemic diseases. Automated analysis and interpretation of fundus images has become a necessary and important diagnostic procedure in ophthalmology. Among the features in ocular fundus image are the optic disc, fovea (central vision area), lesions, and retinal vessels. These features are useful in revealing the states of diseases in the form of measurable abnormalities such as length of diameter, change in color, and degree of tortuosity in the vessels. In addition, retinal vessels can also serve as landmarks for image-guided laser treatment of choroidal neovascularization. Thus, reliable methods for blood vessel detection that preserve various vessel measurements are needed. In this paper, we will examine the pathological issues in the analysis of retinal vessels in digital fundus images and give a survey of current image processing methods for extracting vessels in retinal images with a view to categorize them and highlight their differences and similarities. We have also implemented two major approaches using matched filter and mathematical morphology respectively and compared their performances. Some prospective research directions are identified. / Singapore-MIT Alliance (SMA)

This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.

We present investigation and designing of a system that interacts with big audience, sitting in a dimmed theater environment. The goal is to automatically detect audiences and some of their actions. Test results indicate that because of low light condition we can’t rely on RGB camera footage in a dimmed environment. We use Microsoft Kinect Sensor to collect data from environment. Kinect is designed to be used with Microsoft Xbox 360 for gaming purposes. It has both RGB and Infrared depth camera. Change in amount of visible light doesn’t affect data from depth camera. Kinect is not a strong camera so it has limitations that we should deal with. Viewing angles of both cameras and depth range of Infrared camera are limited. Viewing angles of depth camera are 43° vertical and 57° horizontal. Most accurate range of depth camera is 1 meter to 4 meters from camera. Non-infrared reflective surfaces cause gaps in depth data. We evaluate possibility of using Kinect camera in a large environment with big audience. “Dome 3D theater” in Norrkoping Visualization Center C, is selected as environment to investigate and test the system. We ran some tests to find the best place and best height for camera to have most coverage. Our system works with optimized image processing algorithms that use 3D depth data instead of regular RGB or Grayscale image. We use “libfreenect”, Open Kinect library to get Kinect sensor up and running. C++ and OpenGL are used as programing languages and graphics interface, respectively. Open GLUT (OpenGL Utility Toolkit) is used for system’s user interface. It was not possible to use Dome environment for every test during the programming period so we recorded some depth footage and used for later tests. While evaluating the possibility of using Kinect in Dome environment, we realized that implementing a voting system would make a good demonstration and test application. Our system counts votes after audiences raise their hands to vote for something.

In today's industry 3D cameras are often used to inspect products. The camera produces both a 3D model and an intensity image by capturing a series of profiles of the object using laser triangulation. In many of these setups a physical encoder is attached to, for example, the conveyor belt that the product is travelling on. The encoder is used to get an accurate reading of the speed that the product has when it passes through the laser. Without this, the output image from the camera can be distorted due to a variation in velocity. In this master thesis a method for integrating the functionality of this physical encoder into the software of the camera is proposed. The object is scanned together with a pattern, with the help of this pattern the object can be restored to its original proportions. / I dagens industri används ofta 3D-kameror för att inspektera produkter. Kameran producerar en 3D-modell samt en intensitetsbild genom att sätta ihop en serie av profilbilder av objektet som erhålls genom lasertriangulering. I många av dessa uppställningar används en fysisk encoder som återspeglar hastigheten på till exempel transportbandet som produkten ligger på. Utan den här encodern kan bilden som kameran fångar bli förvrängd på grund av hastighetsvariationer. I det här examensarbetet presenteras en metod för att integrera funktionaliteten av encodern in i kamerans mjukvara. För att göra detta krävs att ett mönster placeras längs med objektet som ska bli skannat. Mönstret återfinns i bilden fångad av kameran och med hjälp av detta mönster kan hastigheten bestämmas och objektets korrekta proportioner återställas.

This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The second shortcoming is the non-examination of the vector angle as a distance measure that is capable of directly evaluating hue similarity without considering intensity especially in RGB. Finally, there is inadequate research on the combination of hue- and intensity-based similarity measures to improve color similarity calculations given the advantages of each color distance measure. These distance measures were used for two image understanding tasks: edge detection, and one strategy for color image segmentation, namely color clustering. Edge detection algorithms using Euclidean distance and vector angle similarity measures as well as their combinations were examined. The list of algorithms is comprised of the modified Roberts operator, the Sobel operator, the Canny operator, the vector gradient operator, and the 3x3 difference vector operator. Pratt's Figure of Merit is used for a quantitative comparison of edge detection results. Color clustering was examined using the k-means (based on the Euclidean distance) and Mixture of Principal Components (based on the vector angle) algorithms. A new quantitative image segmentation evaluation procedure is introduced to assess the performance of both algorithms. Quantitative and qualitative results on many color images (artificial, staged scenes and natural scene images) indicate good edge detection performance using a vector version of the Sobel operator on the h1h2h3 color space. The results using combined hue- and intensity-based difference measures show a slight improvement qualitatively and over using each measure independently in RGB. Quantitative and qualitative results for image segmentation on the same set of images suggest that the best image segmentation results are obtained using the Mixture of Principal Components algorithm on the RGB, XYZ and rgb color spaces. Finally, poor color clustering results in the h1h2h3 color space suggest that some assumptions in deriving a simplified version of the Dichromatic Reflectance Model might have been violated.

In about 30 years of experiments and development, impulse drying is now considered as a well known technology and a good candidate in the constant effort to save energy in the paper industry. The drying section is indeed the most expensive section in the process of paper production. However, this potential technology has a major disadvantage, stopping its implementation in the industry. Paper, which is a porous material with a variable compressibility, experienced a sudden release of energy at the nip opening during impulse drying. Under these conditions of high intensity process (both in temperature and pressure), the fiber mat has a tendency to delaminate. This web disruption is a critical issue against impulse drying. This thesis comes up with a new approach to the problem. These last years, the technology itself has been addressed in this issue and many improvements have been reached in terms of energy release (heat transfer control, material coating ). The novel idea is then to investigate the inner structure of the paper once it has been coated with starch to a large extent (up to 10 or 20% of the relative basis weight). Starch is known for its large use in industry, but also its capability to expand under high temperature. Hence, both relative strength and bulking effects are investigated in this thesis, using numerous experiments with variable temperatures and pressures, along with ultrasonic testing and image analysis. We have the opportunity to appreciate the phenomenon of heat transfer and mass transport in the coated medium, while reaching promising results in terms of strength and bulk. These are finally investigated using scanning electron microscopy as a first step toward a pore expansion model for starch imbued handsheets.

A method for detecting buildings from satellite/aerial images is proposed in this study. The aim is to extract rectilinear buildings by using hypothesize first verify next manner. Hypothesis generation is accomplished by using edge detection and line generation stages. Hypothesis verification is carried out by using information obtained both from the color segmentation of HSV representation of the image and the shadow detection stages&rsquo / output. Satellite/aerial image is firstly filtered to sharpen the edges. Then, edges are extracted using Canny edge detection algorithm. These edges are the input for the Hough Transform stage which will produce line segments according to these extracted edges. Then, extracted line segments are used to generate building hypotheses. Verification of these hypotheses makes use of the outputs of the HSV color segmentation and shadow detection stages. In this study, color segmentation is processed on the HSV representation of the satellite/aerial image which is less sensitive to illumination. In order to perform the shadow detection, the basic information which is shadow areas have higher value of saturation component and lower value of value component in HSV color space is used and according to this information a mask is applied to the HSV representation of the image to produce shadow pixels. The proposed method is implemented as software written in MATLAB programming software. The approach was tested in several different areas. The results are encouraging.