This diploma thesis inspects problems with specification location of edges with higher accuracy then one pixel (subpixel accuracy). In terms of this assignment has been created program, which generates three different shapes of objects. With change of parameters in program is measuring location of gravitational center on objects with subpixel accuracy. Obtained data of gravitational center deviations are depictured in graphs.

This work deals with the possibilities of employing a genetic algorithm to edge detection. There is introduced a project which uses enhanced image divided into sub-regions, on which detection by genetic algorithm is applied. To achieving our goals are used individuals in two-dimensional bit arrays, for which are specially adjusted mutation and crossover operators. Cost minimization approach is used as fitness function. The project was created and tested in Matlab environment.

The goal of this work is to investigate options how to apply trainable edge detection algorithm Structured forest for fast edge detection to information extraction from historici maps and medical images. For the work, annotated dataset was created and the detektor was tested on it. Structured forest achieved better results on map data, compared with classical detectors. Success rate of finding edges of bones was similar at both approaches. Aim of the work is focused on comparing different image annotation styles, experiments with dataset, including determining parameters and evaluation of the methods.

Edge detection is one of the most important application in image processing. Field-Programmable Gate Arrays (FPGAs) have become popular computing platforms for signal and image processing. The Zynq-7000 System on Chip (SOC) is a dual-processor platform with shared memory. The thesis describes a novel and fast implementation of Sobel edge detection using the Zynq-7000 SoC. Our implementation is a combination of software and hardware using the Vivado HLS and Zynq (SoC). As a result our implementation is fast. We make a comparison with other conventional edge detection techniques and show that the speed of operation of this design is much faster.

Placement of virtual objects in Augmented Reality is often done without regarding the artifacts in the physical environment. This thesis investigates how placement can be done with the artifacts included. It only considers placement of wall mounted objects. Through the development of two prototypes, using detected edges in RGB-images in combination with volumetric properties to identify the artifacts, arreas will be suggested for placement of virtual objects. The first prototype analyze each triangle in the model, which is an intensive and with low precision on the localization of the physical artifacts. The second prototype analyzed the detected RGB-edges in world space, which proved to detect the features with precise localization and a reduce calculation time. The second prototype manages this in a controlled setting. However, a more challenging environment would possibly pose other issues. In conclusion, placement in relation to volumetric and edge information from images in the environment is possible and could enhance the experience of being in a mixed reality, where physical and virtual objects coexist in the same world. / Placering av virtuella objekt i Augumented Reality görs ofta utan att ta hänsyn till objekt i den fysiska miljön. Den här studien utreder hur placering kan göras med hänsyn till den fysiska miljön och dess objekt. Den behandlar enbart placering av objekt på vertikala ytor. För undersökningen utvecklas två prototyper som använder sig av kantigenkänning i foton samt en volymmetrisk representation av den fysiska miljön. I denna miljö föreslår prototyperna var placering av objekt kan ske. Den första prototypen analyserar varje triangel i den volymmetriska representationen av rummet, vilket visade sig vara krävande och med låg precision av lokaliseringen av objekt i miljön. Den andra prototypen analyserar de detekterade kanterna i fotona och projicerar dem till deras positioner i miljön. Vilket var något som visade sig hitta objekt i rummet med god precision samt snabbare än den första prototypen. Den andra prototypen lyckas med detta i en kontrollerad miljö. I en mer komplex och utmanande miljö kan problem uppstå. Placering av objekt i Augumented Reality med hänsyn till både en volymmetrisk och texturerad representation av en miljö kan uppnås. Placeringen kan då ske på ett mer naturligt sätt och därmed förstärka upplevelsen av att virtuella och verkliga objekt befinner sig i samma värld.

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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.

Event Detection is a process of identifying terrain flatness from which localized events such as potholes in the terrain surface can be found and is an important tool in pavement health monitoring and vehicle performance inspection. Repeated detection of terrain surfaces over an extended period of time can be used by highway engineers for long term road health monitoring. An accurate terrain map can allow maintenance personnel for identifying deterioration in road surface for immediate correction. Additionally, knowledge of the events in terrain surface can be used to predict the performance the vehicles would experience while traveling over it. Event detection is composed of two processes: event edging and stitching edges to events. Edge detection is a process of identifying significant localized changes in the terrain surface. Many edge detection methods have been designed capable of capturing edges in terrain surfaces. Gradient searches are frequently used in image processing to recover useful information from images. The issue with using a gradient search method is that it returns deterministic values resulting in edges which are less precise. In order to predict the precision of the terrain surface, the individual nodal probability densities must be quantified and finally combined for the precision of terrain surface. A Comparative Nodal Uncertainty Method is developed in this work to detect edges based on the probability distribution of the nodal heights within some local neighborhood. Edge stitching is developed to group edges to events in a correct sequence from which an event can be determined finally. / Master of Science