Electron backscatter diffraction (EBSD) is a common tool used by phy- sicists to examine crystalline materials, which is based on taking pictures of material microstructure using electron microscope. To determine additional characteristics of studied specimen, a specific variant called High resolution EBSD has been proposed (and partially adopted). The technique takes se- veral subregions of the images taken by the EBSD camera and uses cross- correlation to measure deformation of obtained patterns. Usability of this method is limited by its relatively high computational complexity, which makes it useless for the analysis of larger specimen surfaces. At the same time, processing of individual subregions and images is independent, which makes it appropriate for parallelization provided by modern GPUs. In this thesis, we describe the technique used to process the EBSD data in detail, analyze it and implement the most computationally demanding parts using the CUDA technology. Compared to a reference Python implementation, we measured a speedup of 30-40-times when using a double floating precision and up to a 270-times speedup for a single precision.

The work focuses on the analysis of paintings to determine the painting techniques. Specifically, it focuses on the localization of the underdrawing by comparing images taken in the spectra with different penetration depth. Defines the problem associated with the capture of the compared images in different spectra. Specifies methods that determine the dependence between two parts of the spectrum (mainly RGB and IR) and based on the dependence approximates conversion between these two parts of the spectrum (Red spectral component projection, Colour intensity, Weighted average of spectral components, Table conversion, Linear regression, PCA analysis and Edge decomposition). Work also describes more general problems that complicate solving tasks, such as noise, non-uniform illumination and adding the same type of radiation. Problems at work are thoroughly analyzed. We design a Calculation of illumination parameters using a neural network, Approximation of illumination by blur, Polynomial approximation of illumination and TWMJ approximation of illumination for suppressing non-uniform illumination. Define methods Estimation by edge decomposition and Local least squares method solving adding the same type of radiation. In addition, we describe the Gaussian filter, the Averaging, Median filter, Conservative...

Thesis (Ph.D.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

This thesis investigates the potential of fractal dimension (FD) as a tool for enhancing airborne magnetic data. More specifically, this thesis investigates the potential of FD-based texture transform images as tools for aiding in the interpretation of airborne magnetic data. A series of different methods of estimating FD are investigated, specifically: • geometric methods (1D and 2D variation methods and 1D line divider method); • stochastic methods (1D and 2D Hurst methods and 1D and 2D semi-variogram methods), and; • spectral methods (1D and 2D wavelet methods and 1D and 2D Gabor methods). All of these methods are able to differentiate between varying theoretical FD in synthetic profiles. Moreover, these methods are able to differentiate between theoretical FDs when applied to entire profiles or in a moving window along the profile. Generally, the accuracy of the estimated FD improves when window size is increased. Similarly, the standard deviation of estimated FD decreases as window size increases. This result implied that the use of moving window FD estimates will require a trade off between the quality of the FD estimates and the need to use small windows to allow better spatial resolution. Application of the FD estimation methods to synthetic datasets containing simple ramps, ridges and point anomalies demonstrates that all of the 2D methods and most of the 1D methods are able to detect and enhance these features in the presence of up to 20% Gaussian noise. In contrast, the 1D Hurst and line divider methods can not clearly detect these features in as little as 10% Gaussian noise. Consequently, it is concluded that the 1D Hurst and line divider methods are inappropriate for enhancing airborne magnetic data. The application of these methods to simple synthetic airborne magnetic datasets highlights the methods’ sensitivity to very small variations in the data. All of the methods responded strongly to field lines some distance from the causative magnetic bodies. This effect was eliminated through the use of a variety of tolerances that essentially required a minimum level of difference between data points in order for FD to be calculated. Whilst this use of tolerances was required for synthetic datasets, its use was not required for noise corrupted versions of the synthetic magnetic data. The results from applying the FD estimation techniques to the synthetic airborne magnetic data suggested that these methods are more effective when applied to data from the pole. Whilst all of the methods were able to enhance the magnetic anomalies both at the pole and in the Southern hemisphere, the responses of the FD estimation techniques were notably simpler for the polar data. With the exception of the 1D Hurst and line divider methods, all of the methods were also able to enhance the synthetic magnetic data in the presence of 10% Gaussian noise. Application of the FD estimation methods to an airborne magnetic dataset from the Merlinleigh Sub-basin in Western Australia demonstrated their ability to enhance subtle structural features in relatively smooth airborne magnetic data. Moreover, the FD-based enhancements were able to enhance some features of this dataset better than any of the conventional enhancements considered (i.e. an analytic signal, vertical and total horizontal derivatives, and automatic gain control). Most of the FD estimation techniques enhanced similar features to each other. However, the 2D methods generally produced clearer results than their associated 1D methods. In contrast to this result, application of the FD-based enhancements to more variable airborne magnetic data from the Tanami region in the Northern Territory demonstrated that these methods are not as well suited to this style of data. The main conclusion from this work is that FD-based enhancement of relatively smooth airborne magnetic data can provide valuable input into an interpretation process. This suggests that these methods are particularly useful for aiding in the interpretation of airborne magnetic data from regions such as sedimentary basins where the distribution of magnetic sources is relatively smooth and simple. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1339560 / Thesis (Ph.D.) - University of Adelaide, Australian School of Petroleum, 2008

The thesis is concerned with optical character recognition from image data with different methods used for character classification. In the first theoretical part it focuses on explanation of all important parts of system for optical character recognition. The latter practical part of the thesis describes an example of image segmentation, the implementation of artificial neural networks for image recognition and create simple training set of data for the evaluation of the network. It also describes the process of training Tesseract tool and its implementation in a simple application EasyTessOCR for character recognition.

Products with complicated shapes require superior surface finish to perform the intended function. Despite significant developments in technology, finishing operations are still performed semi automatically/manually, relying on the skills of the machinist. The pressure to produce products at the best quality in the shortest lead time has made it highly inconvenient to depend on traditional methods. Thus, there is a rising need for automation which has become a resource to remain competitive in the manufacturing industry. Diminishing return of trading quality over time in finishing operations signifies the importance of having a pre-determined trajectory (tool path) that produces an optimum surface in the least possible machining time. Tool path optimization for finishing process considering tool kinematics is of relatively low importance in the present scenario. The available automation in grinding processes encompass around the dynamics of machining. In this paper we provide an overview of optimizing the tool path using evolutionary algorithms, considering the significance of process dynamics and kinematics. Process efficiency of the generated tool movements are studied based on the evaluation of relative importance of the finishing parameters. Surface quality is analysed using MATLAB and optimization is performed on account of peak to valley height. Surface removal characteristics are analysed based on process variables that have the most likely impact on surface finish. The research results indicated that tool path is the most significant parameter determining the surface quality of a finishing operation. The inter-dependency of parameters were also studied using Taguchi design of experiments. Possible combinations of various tool paths and tool influencing parameters are presented to realize a surface that exhibits lowest errors. / European Horizon 2020 Project SYMPLEXITY