The analysis of microstructural shapes is an underutilized tool in the field of materials science. Typical observations of morphology are qualitative, rather than quantitative, which prevents the identification of relationships between shape and the mechanical properties of a material. Recent advances in the fields of computer vision and high-dimensional analysis have made computer-based shape characterization feasible on a variety of materials. In this work, the relationship between microstructural shapes, and the properties and function of the material as a whole, is explored using moment invariants as global shape descriptors. A diifferent relationship is examined in each of three material systems: how the three-dimensional shapes of cells in the cotyledons of the plant Arabidopsis Thaliana can be used to identify cell function; the two-dimensional shapes of additive manufacturing feedstock powder and the ability to distinguish between images of powders from different samples; and the two-dimensional shapes of ' precipitates and their influence on the creep resistance of single crystal nickel-base superalloys. In the case of Arabidopsis Thaliana cotyledon cells, three-dimensional Zernike and Cartesian moment invariants were used to quantify morphology, and combined with size and orientation information. These feature sets were then analyzed using unsupervised and supervised machine learning methods. Moderate success was found using unsupervised methods, indicating that natural delineations in the data correlate to cell roles to some degree. Using supervised methods, a success rate of 90% was possible, indicating that these features can be used to identify cell function. The ability of two-dimensional Cartesian moment invariants to distinguish meaningful features in particles of additive manufacturing feedstock was tested by using these features to classify images of feedstock. Ultimately, simple histogram matching methods were unsuccessful, likely because they rely on the most common particles to draw conclusions. A bag-of-words method was used, which uses high-dimensional visualization and clustering techniques to classify individual particles by common features. Histograms of particle clusters are then used to represent each image. This method was far more successful, and a correct classification rate of up to 90% was found, and comparable rates were discovered using invariants which describe the shapes only broadly. This indicates that moment invariants are an effective measure of the morphologies of these types of particles, and can be used to classify powder shapes, which control many properties which are relevant to the additive manufacturing process. In the case of the superalloys, it has been shown that the shape distribution of ' precipitates can be tracked using second order moment invariants. In addition, several loworder moment invariants are shown to correlate to creep resistance in four alloys examined, which supports the idea that the shape of precipitates plays role in determining creep resistance in these alloys.

Poverty and long distances are two reasons why some people in the third world countries hasdifficulties seeking medical help. A solution to the long distances could be if the medical carewas more mobile and diagnostically tests could be performed on site in villages. A new pointof-care test based on a small blood shows promising results both in run time and mobility.However, the method still needs more advanced equipment for analysis of the resultingmicroarray. This study has investigated the potential to perform the analysis within asmartphone application, performing all steps from image capturing to a diagnostic result. Theproject was approach in two steps, starting with implementation and selection of imageanalysis methods and finishing with implementing those results into an Android application.A final application was not developed, but the results gained from this project indicates that asmartphone processing power is enough to perform heavy image analysis within a sufficientamount of time. It also imply that the resolution in the evaluated images taken with a Nexus 6together with an external macro lens most likely is enough for the whole analysis, but furtherwork must be done to ensure it.

Os projetistas de sistemas digitais enfrentam sempre o desafio de encontrar o balanço correto entre velocidade e generalidade de processamento de seu hardware. Originalmente dispositivos de lógica programável de alta densidade como FPGAs (Field Programable Gate Arrays) e CPLDs (Complex Logic Programmable Devices) vinham sendo utilizados como dispositivos de lógica acoplada(glue logic), reduzindo significantemente o número de componentes em um sistema. Seu uso como forma de substituir arquiteturas já existentes de microcontroladores e microprocessadores já é uma realidade. A representação e reconhecimento de objetos em imagens de duas dimensões é um tópico importante. Uma forma comum de se fazer a representação de um objeto ou uma imagem é a utilização de momentos da função de intensidade de um grupo de pixels. Devido ao alto custo computacional para o cálculo desses momentos tem sido importante a busca por arquiteturas que de alguma forma agilizem o cálculo dos mesmos. Um problema enfrentado por arquiteturas desenvolvidas atualmente para trabalhar em forma de periférico com um computador pessoal (PC) ou uma estação de trabalho é a velocidade do barramento de transferência de dados. Interfaces de uso mais simples, como USB (Universal Serial Bus) ou Ethernet, têm sua taxa de transferência na casa dos megabytes por segundo. Uma solução para esse problema é o uso do barramento PCI, as transferências feitas nesse barramento podem chegar à casa dos gigabytes por segundo. Esse trabalho vem apresentar uma arquitetura, em forma de soft core totalmente compatível com o padrão Wishbone, para a extração de características invariantes em imagens binárias utilizando-se de dispositivos de lógica programável complexa. Desse modo torna-se possível o uso do barramento PCI para a transmissão de dados para um microcomputador ou uma estação de trabalho. / A challenge for digital systems designers is to meet the balance between speed and flexibility was always. FPGAs and CPLDs where used as glue logic, reducing the number of components in a system. The use of programmable logic (CPLDs and FPGAs) as an alternative to microcontrollers and microprocessors is a real issue. Moments of the intensity function of a group of pixels have been used for the representation and recognition of objects in two dimensional images. Due to the high cost of computing the moments, the search for faster computing architectures is very important. A problem faced by nowadays developed architectures is the speed of computer communication buses. Simpler interfaces, as USB (Universal Serial Bus) and Ethernet, have their transfer rate in megabytes per second. A solution for this problem is the use the PCI bus, where the transfer rate can achieve gigabytes per second. This work presents a soft core architecture, fully compatible with the Wishbone standard, for the extraction of invariant characteristics from binary images using logic programmable devices.

Inspection of LCD light-guide plate using digital image processing is proposed. Binary dot-pattern images from SEM observation are obtained by image segmentation. Pattern recognition for the images is then performed using moment invariants, Bayes classifier, and Neural network. The rotation independent classification for the recognition using only one descript shape factor are also proposed to reduce storage space. It is found the method has been applied successfully in inspection of different defects on the plate subject to any rotation angles and image scales.

The goal of this thesis is the development of a fast and robust algorithm that is able to detect patterns in flow fields independent from their orientation and adequately visualize the results for a human user. This thesis is an interdisciplinary work in the field of vector field visualization and the field of pattern recognition. A vector field can be best imagined as an area or a volume containing a lot of arrows. The direction of the arrow describes the direction of a flow or force at the point where it starts and the length its velocity or strength. This builds a bridge to vector field visualization, because drawing these arrows is one of the fundamental techniques to illustrate a vector field. The main challenge of vector field visualization is to decide which of them should be drawn. If you do not draw enough arrows, you may miss the feature you are interested in. If you draw too many arrows, your image will be black all over. We assume that the user is interested in a certain feature of the vector field: a certain pattern. To prevent clutter and occlusion of the interesting parts, we first look for this pattern and then apply a visualization that emphasizes its occurrences. In general, the user wants to find all instances of the interesting pattern, no matter if they are smaller or bigger, weaker or stronger or oriented in some other direction than his reference input pattern. But looking for all these transformed versions would take far too long. That is why, we look for an algorithm that detects the occurrences of the pattern independent from these transformations. In the second part of this thesis, we work with moment invariants. Moments are the projections of a function to a function space basis. In order to compare the functions, it is sufficient to compare their moments. Normalization is the act of transforming a function into a predefined standard position. Moment invariants are characteristic numbers like fingerprints that are constructed from moments and do not change under certain transformations. They can be produced by normalization, because if all the functions are in one standard position, their prior position has no influence on their normalized moments. With this technique, we were able to solve the pattern detection task for 2D and 3D flow fields by mathematically proving the invariance of the moments with respect to translation, rotation, and scaling. In practical applications, this invariance is disturbed by the discretization. We applied our method to several analytic and real world data sets and showed that it works on discrete fields in a robust way.

Os projetistas de sistemas digitais enfrentam sempre o desafio de encontrar o balanço correto entre velocidade e generalidade de processamento de seu hardware. Originalmente dispositivos de lógica programável de alta densidade como FPGAs (Field Programable Gate Arrays) e CPLDs (Complex Logic Programmable Devices) vinham sendo utilizados como dispositivos de lógica acoplada(glue logic), reduzindo significantemente o número de componentes em um sistema. Seu uso como forma de substituir arquiteturas já existentes de microcontroladores e microprocessadores já é uma realidade. A representação e reconhecimento de objetos em imagens de duas dimensões é um tópico importante. Uma forma comum de se fazer a representação de um objeto ou uma imagem é a utilização de momentos da função de intensidade de um grupo de pixels. Devido ao alto custo computacional para o cálculo desses momentos tem sido importante a busca por arquiteturas que de alguma forma agilizem o cálculo dos mesmos. Um problema enfrentado por arquiteturas desenvolvidas atualmente para trabalhar em forma de periférico com um computador pessoal (PC) ou uma estação de trabalho é a velocidade do barramento de transferência de dados. Interfaces de uso mais simples, como USB (Universal Serial Bus) ou Ethernet, têm sua taxa de transferência na casa dos megabytes por segundo. Uma solução para esse problema é o uso do barramento PCI, as transferências feitas nesse barramento podem chegar à casa dos gigabytes por segundo. Esse trabalho vem apresentar uma arquitetura, em forma de soft core totalmente compatível com o padrão Wishbone, para a extração de características invariantes em imagens binárias utilizando-se de dispositivos de lógica programável complexa. Desse modo torna-se possível o uso do barramento PCI para a transmissão de dados para um microcomputador ou uma estação de trabalho. / A challenge for digital systems designers is to meet the balance between speed and flexibility was always. FPGAs and CPLDs where used as glue logic, reducing the number of components in a system. The use of programmable logic (CPLDs and FPGAs) as an alternative to microcontrollers and microprocessors is a real issue. Moments of the intensity function of a group of pixels have been used for the representation and recognition of objects in two dimensional images. Due to the high cost of computing the moments, the search for faster computing architectures is very important. A problem faced by nowadays developed architectures is the speed of computer communication buses. Simpler interfaces, as USB (Universal Serial Bus) and Ethernet, have their transfer rate in megabytes per second. A solution for this problem is the use the PCI bus, where the transfer rate can achieve gigabytes per second. This work presents a soft core architecture, fully compatible with the Wishbone standard, for the extraction of invariant characteristics from binary images using logic programmable devices.

The goal of this thesis is the development of a fast and robust algorithm that is able to detect patterns in flow fields independent from their orientation and adequately visualize the results for a human user. This thesis is an interdisciplinary work in the field of vector field visualization and the field of pattern recognition. A vector field can be best imagined as an area or a volume containing a lot of arrows. The direction of the arrow describes the direction of a flow or force at the point where it starts and the length its velocity or strength. This builds a bridge to vector field visualization, because drawing these arrows is one of the fundamental techniques to illustrate a vector field. The main challenge of vector field visualization is to decide which of them should be drawn. If you do not draw enough arrows, you may miss the feature you are interested in. If you draw too many arrows, your image will be black all over. We assume that the user is interested in a certain feature of the vector field: a certain pattern. To prevent clutter and occlusion of the interesting parts, we first look for this pattern and then apply a visualization that emphasizes its occurrences. In general, the user wants to find all instances of the interesting pattern, no matter if they are smaller or bigger, weaker or stronger or oriented in some other direction than his reference input pattern. But looking for all these transformed versions would take far too long. That is why, we look for an algorithm that detects the occurrences of the pattern independent from these transformations. In the second part of this thesis, we work with moment invariants. Moments are the projections of a function to a function space basis. In order to compare the functions, it is sufficient to compare their moments. Normalization is the act of transforming a function into a predefined standard position. Moment invariants are characteristic numbers like fingerprints that are constructed from moments and do not change under certain transformations. They can be produced by normalization, because if all the functions are in one standard position, their prior position has no influence on their normalized moments. With this technique, we were able to solve the pattern detection task for 2D and 3D flow fields by mathematically proving the invariance of the moments with respect to translation, rotation, and scaling. In practical applications, this invariance is disturbed by the discretization. We applied our method to several analytic and real world data sets and showed that it works on discrete fields in a robust way.

Titanium-7%wt-Aluminum (Ti-7Al) has been of interest to the aerospace industry owing to its good structural and thermal properties. However, extensive research is still needed to study the structural behavior and determine the material properties of Ti-7Al. The homogenized macro-scale material properties are directly related to the crystallographic structure at the micro-scale. Furthermore, microstructural uncertainties arising from experiments and computational methods propagate on the material properties used for designing aircraft components. Therefore, multi-scale modeling is employed to characterize the microstructural features of Ti-7Al and computationally predict the macro-scale material properties such as Young's modulus and yield strength using machine learning techniques. Investigation of microstructural features across large domains through experiments requires rigorous and tedious sample preparation procedures that often lead to material waste. Therefore, computational microstructure reconstruction methods that predict the large-scale evolution of microstructural topology given the small-scale experimental information are developed to minimize experimental cost and time. However, it is important to verify the synthetic microstructures with respect to the experimental data by characterizing microstructural features such as grain size and grain shape. While the relationship between homogenized material properties and grain sizes of microstructures is well-studied through the Hall-Petch effect, the influences of grain shapes, especially in complex additively manufactured microstructure topologies, are yet to be explored. Therefore, this work addresses the gap in the mathematical quantification of microstructural topology by developing measures for the computational characterization of microstructures. Moreover, the synthesized microstructures are modeled through crystal plasticity simulations to determine the material properties. However, such crystal plasticity simulations require significant computing times. In addition, the inherent uncertainty of experimental data is propagated on the material properties through the synthetic microstructure representations. Therefore, the aforementioned problems are addressed in this work by explicitly quantifying the microstructural topology and predicting the material properties and their variations through the development of surrogate models. Next, this work extends the proposed multi-scale models of microstructure-property relationships to magnetic materials to investigate the ferromagnetic-paramagnetic phase transition. Here, the same Ising model-based multi-scale approach used for microstructure reconstruction is implemented for investigating the ferromagnetic-paramagnetic phase transition of magnetic materials. The previous research on the magnetic phase transition problem neglects the effects of the long-range interactions between magnetic spins and external magnetic fields. Therefore, this study aims to build a multi-scale modeling environment that can quantify the large-scale interactions between magnetic spins and external fields. / Doctor of Philosophy / Titanium-Aluminum (Ti-Al) alloys are lightweight and temperature-resistant materials with a wide range of applications in aerospace systems. However, there is still a lack of thorough understanding of the microstructural behavior and mechanical performance of Titanium-7wt%-Aluminum (Ti-7Al), a candidate material for jet engine components. This work investigates the multi-scale mechanical behavior of Ti-7Al by computationally characterizing the micro-scale material features, such as crystallographic texture and grain topology. The small-scale experimental data of Ti-7Al is used to predict the large-scale spatial evolution of the microstructures, while the texture and grain topology is modeled using shape moment invariants. Moreover, the effects of the uncertainties, which may arise from measurement errors and algorithmic randomness, on the microstructural features are quantified through statistical parameters developed based on the shape moment invariants. A data-driven surrogate model is built to predict the homogenized mechanical properties and the associated uncertainty as a function of the microstructural texture and topology. Furthermore, the presented multi-scale modeling technique is applied to explore the ferromagnetic-paramagnetic phase transition of magnetic materials, which causes permanent failure of magneto-mechanical components used in aerospace systems. Accordingly, a computational solution is developed based on an Ising model that considers the long-range spin interactions in the presence of external magnetic fields.

Visual tracking is one of the most important applications of computer vision. Several tracking systems have been developed which either focus mainly on the tracking of targets moving on a plane, or attempt to reduce the 3-dimensional tracking problem to the tracking of a set of characteristic points of the target. These approaches are seriously handicapped in complex visual situations, particularly those involving significant perspective, textures, repeating patterns, or occlusion. This dissertation describes a new approach to visual tracking for monocular and binocular image sequences, and for both passive and active cameras. The method combines Kalman-type prediction with steepest-descent search for correspondences, using 2-dimensional affine mappings between images. This approach differs significantly from many recent tracking systems, which emphasize the recovery of 3-dimensional motion and/or structure of objects in the scene. We argue that 2-dimensional area-based matching is sufficient in many situations of interest, and we present experimental results with real image sequences to illustrate the efficacy of this approach. Image matching between two images is a simple one to one mapping, if there is no occlusion. In the presence of occlusion wrong matching is inevitable. Few approaches have been developed to address this issue. This dissertation considers the effect of occlusion on tracking a moving object for both monocular and binocular image sequences. The visual tracking system described here attempts to detect occlusion based on the residual error computed by the matching method. If the residual matching error exceeds a user-defined threshold, this means that the tracked object may be occluded by another object. When occlusion is detected, tracking continues with the predicted locations based on Kalman filtering. This serves as a predictor of the target position until it reemerges from the occlusion again. Although the method uses a constant image velocity Kalman filtering, it has been shown to function reasonably well in a non-constant velocity situation. Experimental results show that tracking can be maintained during periods of substantial occlusion. The area-based approach to image matching often involves correlation-based comparisons between images, and this requires the specification of a size for the correlation windows. Accordingly, a new approach based on moment invariants was developed to select window size adaptively. This approach is based on the sudden increasing or decreasing in the first Maitra moment invariant. We applied a robust regression model to smooth the first Maitra moment invariant to make the method robust against noise. This dissertation also considers the effect of spatial quantization on several moment invariants. Of particular interest are the affine moment invariants, which have emerged, in recent years as a useful tool for image reconstruction, image registration, and recognition of deformed objects. Traditional analysis assumes moments and moment invariants for images that are defined in the continuous domain. Quantization of the image plane is necessary, because otherwise the image cannot be processed digitally. Image acquisition by a digital system imposes spatial and intensity quantization that, in turn, introduce errors into moment and invariant computations. This dissertation also derives expressions for quantization-induced error in several important cases. Although it considers spatial quantization only, this represents an important extension of work by other researchers. A mathematical theory for a visual tracking approach of a moving object is presented in this dissertation. This approach can track a moving object in an image sequence where the camera is passive, and when the camera is actively controlled. The algorithm used here is computationally cheap and suitable for real-time implementation. We implemented the proposed method on an active vision system, and carried out experiments of monocular and binocular tracking for various kinds of objects in different environments. These experiments demonstrated that very good performance using real images for fairly complicated situations. / Ph. D.

The goal of this thesis is the development of a fast and robust algorithm that is able to detect patterns in flow fields independent from their orientation and adequately visualize the results for a human user. This thesis is an interdisciplinary work in the field of vector field visualization and the field of pattern recognition. A vector field can be best imagined as an area or a volume containing a lot of arrows. The direction of the arrow describes the direction of a flow or force at the point where it starts and the length its velocity or strength. This builds a bridge to vector field visualization, because drawing these arrows is one of the fundamental techniques to illustrate a vector field. The main challenge of vector field visualization is to decide which of them should be drawn. If you do not draw enough arrows, you may miss the feature you are interested in. If you draw too many arrows, your image will be black all over. We assume that the user is interested in a certain feature of the vector field: a certain pattern. To prevent clutter and occlusion of the interesting parts, we first look for this pattern and then apply a visualization that emphasizes its occurrences. In general, the user wants to find all instances of the interesting pattern, no matter if they are smaller or bigger, weaker or stronger or oriented in some other direction than his reference input pattern. But looking for all these transformed versions would take far too long. That is why, we look for an algorithm that detects the occurrences of the pattern independent from these transformations. In the second part of this thesis, we work with moment invariants. Moments are the projections of a function to a function space basis. In order to compare the functions, it is sufficient to compare their moments. Normalization is the act of transforming a function into a predefined standard position. Moment invariants are characteristic numbers like fingerprints that are constructed from moments and do not change under certain transformations. They can be produced by normalization, because if all the functions are in one standard position, their prior position has no influence on their normalized moments. With this technique, we were able to solve the pattern detection task for 2D and 3D flow fields by mathematically proving the invariance of the moments with respect to translation, rotation, and scaling. In practical applications, this invariance is disturbed by the discretization. We applied our method to several analytic and real world data sets and showed that it works on discrete fields in a robust way.