In this effort a new measurement technique for the lateral Goos-Hanchen shift is developed, analyzed, and demonstrated. The new technique uses classical image formation methods fused with modern detection and analysis methods to achieve higher levels of sensitivity than obtained with prior practice. Central to the effort is a new mathematical model of the dispersion seen at a step shadow when the Goos-Hanchen effect occurs near critical angle for total internal reflection. Image processing techniques are applied to measure the intensity distribution transfer function of a new divergence model of the Goos-Hanchen phenomena providing verification of the model. This effort includes mathematical modeling techniques, analytical derivations of governing equations, numerical verification of models and sensitivities, optical design of apparatus, image processing

Two approaches were developed for detecting earthquake damaged buildings from post-event aerial photographs using shadow analysis and perceptual grouping. In the first approach, it is assumed that the vector boundaries of the buildings are not known a priori. Therefore, only the post-event aerial photographs were used to detect the collapsed buildings. The approach relies on an idea that if a building is fully damaged then, it will not generate a closed contour. First, a median filter is applied to remove the noise. Then, the edge pixels are detected through a Canny edge detector and the line segments are extracted from the output edge image using a raster-to-vector conversion process. After that, the line segments are grouped together using a three-level hierarchical perceptual grouping procedure to form a closed contour. The principles used in perceptual grouping include the proximity, the collinearity, the continuity and the perpendicularity. In the second approach, it is assumed that the vector boundaries of the buildings are known a priori. Therefore, this information is used as additional data source to detect the collapsed buildings. First, the edges are detected from the image through a Canny edge detector. Second, the line segments are extracted using a raster-to-vector conversion process. Then, a two-level hierarchical perceptual grouping procedure is used to group these line segments. The boundaries of the buildings are available and stored in a GIS as vector polygons. Therefore, after applying the perceptual grouping procedure, the damage conditions of the buildings are assessed on a building-by-building basis by measuring the agreement between the detected line segments and the vector boundaries.

A method for detecting the buildings from high resolution aerial images is proposed. The aim is to extract the buildings from high resolution aerial images using the Hough transform and the model based perceptual grouping techniques.The edges detected from the image are the basic structures used in the building detection procedure. The method proposed in this thesis makes use of the basic image processing techniques. Noise removal and image sharpening techniques are used to enhance the input image. Then, the edges are extracted from the image using the Canny edge detection algorithm. The edges obtained are composed of discrete points. These discrete points are vectorized in order to generate straight line segments. This is performed with the use of the Hough transform and the perceptual grouping techniques. The straight line segments become the basic structures of the buildings. Finally, the straight line segments are grouped based on predefined model(s) using the model based perceptual grouping technique. The groups of straight line segments are the candidates for 2D structures that may be the buildings, the shadows or other man-made objects. The proposed method was implemented with a program written in C programming language. The approach was applied to several study areas. The results achieved are encouraging. The number of the extracted buildings increase if the orientation of the buildings are nearly the same and the Canny edge detector detects most of the building edges.If the buildings have different orientations,some of the buildings may not be extracted with the proposed method. In addition to building orientation, the building size and the parameters used in the Hough transform and the perceptual grouping stages also affect the success of the proposed method.

No / Robust techniques are developed to put the H and Ca K line full-disk images taken at the Meudon Observatory into a standardised form of a `virtual solar image'. The techniques include limb fitting, removal of geometrical distortion, centre position and size standardisation and intensity normalisation. The limb fitting starts with an initial estimate of the solar centre using raw 12-bit image data and then applies a Canny edge-detection routine. Candidate edge points for the limb are selected using a histogram based method and the chosen points fitted to a quadratic function by minimising the algebraic distance using SVD. The five parameters of the ellipse fitting the limb are extracted from the quadratic function. These parameters are used to define an affine transformation that transforms the image shape into a circle. Transformed images are generated using the nearest neighbour, bilinear or bicubic interpolation. Intensity renormalisation is also required because of a limb darkening and other non-radial intensity variations. It is achieved by fitting a background function in polar coordinates to a set of sample points having the median intensities and by standardising the average brightness. Representative examples of intermediate and final processed results are presented in addition to the algorithms developed. The research was done for the European Grid of Solar Observations (EGSO) project.

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.

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.

This dissertation has the objective to verify the contribution of different tips for line detection, to the classification of multispectral images, with the possible use in the discrimination and mapping of class of covering of the land. In this context, the comparison between different techniques of detection of characteristics for extraction of power lines in agricultural areas is affected, namely, distinction techniques using variation of contrast and morphologic filtering, and detection of edges using filter Canny and detector SUSAN. In this case were used techniques of extraction of lines with Hough Transform and Radon Transform too, using different algorithms in aerial images of remote sensing. These results had evaluated and compared producing tables of efficiency for each procedure. These tables direct the different solutions that change of boarding depending of the final objective of the detection of the Power Lines. / Essa dissertação tem o objetivo de verificar a contribuição de diferentes abordagens para extração de linhas, à classificação de imagens multiespectrais, com o possível uso na discriminação e mapeamento de classes de cobertura da terra. Nesse contexto, é efetuada a comparação entre diferentes técnicas de extração de características para extração de linhas de transmissão em áreas rurais, a saber, técnicas de realce utilizando variação de contraste e filtragem morfológica, bem como detecção de bordas utilizando filtro Canny e detector SUSAN, citando como técnica de extração de linhas a Transformada de Hough e Transformada de Radon, utilizando diferentes algoritmos, em imagens aéreas e de sensoriamento remoto. O processo de análise de imagens, com diferentes abordagens leva a resultados variados em diferentes tipos de coberturas do solo. Tais resultados foram avaliados e comparados produzindo tabelas de eficiência para cada procedimento. Estas tabelas direcionam a diferentes encaminhamentos, que vão variar de abordagem dependendo do objetivo final da extração das Linhas de Transmissão.

This dissertation has the objective to verify the contribution of different tips for line detection, to the classification of multispectral images, with the possible use in the discrimination and mapping of class of covering of the land. In this context, the comparison between different techniques of detection of characteristics for extraction of power lines in agricultural areas is affected, namely, distinction techniques using variation of contrast and morphologic filtering, and detection of edges using filter Canny and detector SUSAN. In this case were used techniques of extraction of lines with Hough Transform and Radon Transform too, using different algorithms in aerial images of remote sensing. These results had evaluated and compared producing tables of efficiency for each procedure. These tables direct the different solutions that change of boarding depending of the final objective of the detection of the Power Lines. / Essa dissertação tem o objetivo de verificar a contribuição de diferentes abordagens para extração de linhas, à classificação de imagens multiespectrais, com o possível uso na discriminação e mapeamento de classes de cobertura da terra. Nesse contexto, é efetuada a comparação entre diferentes técnicas de extração de características para extração de linhas de transmissão em áreas rurais, a saber, técnicas de realce utilizando variação de contraste e filtragem morfológica, bem como detecção de bordas utilizando filtro Canny e detector SUSAN, citando como técnica de extração de linhas a Transformada de Hough e Transformada de Radon, utilizando diferentes algoritmos, em imagens aéreas e de sensoriamento remoto. O processo de análise de imagens, com diferentes abordagens leva a resultados variados em diferentes tipos de coberturas do solo. Tais resultados foram avaliados e comparados produzindo tabelas de eficiência para cada procedimento. Estas tabelas direcionam a diferentes encaminhamentos, que vão variar de abordagem dependendo do objetivo final da extração das Linhas de Transmissão.

This thesis adresses designing a device that detects lane departure of a vehicle via a video feed from a camera module. This device is intended to be attached onto the windshield of the vehicle. The initial part of the thesis will cover the current methods of lane departure detection through a video feed. In the following part the selection of suitable hardware, specifically the latest model of a Raspberry Pi, has been made. Afterwards a suitable container for the aforementioned hardware has been designed and created using a 3D printer. Subsequently an appropriate LDWS algorithm is chosen and designed. In the next part, the range and parameters of a testing database through which the proper functionality of the device will be tested on are chosen. The final part of the thesis contains evaluation of the success rate of detection via the acquired database.

This diploma thesis deals with the issue of the recognition of road signs in the video sequence. Such systems increase the traffic safety and are implemented by major car factories in the manufactured cars (Opel, BMW). First, the motivation for the utilisation of these systems is presented, followed by the survey of the current state of the art methods. Finally, a specific road-sign detection method is chosen and described in detail. The method uses advanced techniques of signal processing. Segmentation method in color space is used for sign detection and subsequent classification is accomplished by linear classification with optional use of PCA method. In addition, the method contains the prediction of road sign positions based on Kalman filtering. Implemented system yields relatively accurate results and overall analysis and discussion is enclosed.