Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, the ability to model the evolution of microstructures from changes in processing condition or even predict the microstructures from given processing condition would greatly reduce the time frame and the cost of the optimization process. In 1, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. We also demonstrate the capability of predicting new microstructures from the representation with deep learning and we can explore the physical insights of the implicitly expressed microstructure representations. We validate our framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures. In 2, we further improve and generalize the generating framework, a set of metrics is used to quantitatively analyze the effectiveness of the generation by comparing the microstructure characteristics between the generated samples and the originals. We also take advantage of image processing techniques to aid the calculation of metrics that require grain segmentation. / Master of Science / Different from the traditional manufacturing technique which removes material to form the desired shape, additive manufacturing (AM) adds material together to form the shapes usually layer by layer. AM which is sometimes also referred to as 3-D printing enables the optimization of material property through changing the processing conditions. The microstructure is structures formed by materials on a microscopic scale. Crystals like metal usually form a crystalline structure composed of grains where atoms have the same orientation. Especially for metal AM, changes in the processing condition will usually result in changes in microstructures and material properties. To better optimize for the desired material properties, in 1 we present a microstructure representation method that allows projection of microstructure onto the representation space and prediction from an arbitrary point from the representation space. This representation method allows us to better analyze the changes in microstructure in relation to the changes in processing conditions. In 2, we validate the representation and prediction using EBSD data collected from copper samples manufactured with AM under different processing conditions.

Two-dimensional geometric textures are the geometric analogues of raster (pixel-based) textures and consist of planar distributions of discrete shapes with an inherent structure. These textures have many potential applications in art, computer graphics, and cartography. Synthesizing large textures by hand is generally a tedious task. In raster-based synthesis, many algorithms have been developed to limit the amount of manual effort required. These algorithms take in a small example as a reference and produce larger similar textures using a wide range of approaches. Recently, an increasing number of example-based geometric synthesis algorithms have been proposed. I refer to them in this dissertation as Geometric Texture Synthesis (GTS) algorithms. Analogous to their raster-based counterparts, GTS algorithms synthesize arrangements that ought to be judged by human viewers as “similar” to the example inputs. However, an absence of conventional evaluation procedures in current attempts demands an inquiry into the visual significance of synthesized results. In this dissertation, I present an investigation into GTS and report on my findings from three projects. I start by offering initial steps towards grounding texture synthesis techniques more firmly with our understanding of visual perception through two psychophysical studies. My observations throughout these studies result in important visual cues used by people when generating and/or comparing similarity of geometric arrangements as well a set of strategies adopted by participants when generating arrangements. Based on one of the generation strategies devised in these studies I develop a new geometric synthesis algorithm that uses a tile-based approach to generate arrangements. Textures synthesized by this algorithm are comparable to the state of the art in GTS and provide an additional reference in subsequent evaluations. To conduct effective evaluations of GTS, I start by collecting a set of representative examples, use them to acquire arrangements from multiple sources, and then gather them into a dataset that acts as a standard for the GTS research community. I then utilize this dataset in a second set of psychophysical studies that define an effective methodology for comparing current and future geometric synthesis algorithms.

La synthèse de textures est une technique qui génère une texture automatiquement grâce à un algorithme. Cette technique permet de réduire le temps de création des textures et le coût mémoire étant donné que seuls les algorithmes et leurs paramètres ont besoin d'être stockés. Cependant, des difficultés sont souvent rencontrées lors de l'utilisation des textures synthétisées. D'abord, les paramètres de ces textures sont difficiles à manipuler. Ensuite, l'algorithme de synthèse génère souvent les textures sous forme de tableaux de pixels nécessitant beaucoup de mémoire. Pour aborder ces difficultés, nous proposons les approches suivantes : pour améliorer la visualisation de l'espace des textures synthétisées, nous proposons de construire un résumé de cet espace: une seule image statique qui résume, dans un espace limité de pixels, les apparences produites par un synthétiseur donné. De plus, pour améliorer la sélection de paramètres, nous augmentons les curseurs qui contrôlent les paramètres avec des bandes visuelles révélant les changements qui se produisent quand l'utilisateur manipule les curseurs. Pour permettre à l'utilisateur d'interagir de manière interactive avec les résumés visuels, nous nous reposons sur un algorithme de synthèse par patch permettant de générer les textures de façon rapide grâce à une implémentation parallèle sur le processeur graphique. Au lieu de générer le résultat de ce synthétiseur sous forme d'un tableau de pixels, nous représentons le résultat dans une structure compacte et nous utilisons une méthode rapide permettant de lire des pixels directement à partir de cette structure / Texture synthesis is a technique that algorithmically generates textures at rendering time. The automatic synthesis reduces authoring time and memory requirements since only the algorithm and its parameters need to be stored or transferred. However, two difficulties often arise when using texture synthesis: First, the visualization and parameters selection of synthesized textures are difficult. Second, most synthesizers generate textures in a bitmap format leading to high memory usage. To address these difficulties we propose the following approaches: First, to improve the visualization of synthesized textures we propose the idea of a procedural texture preview: A single static image summarizing in a limited pixel space the appearances produced by a given synthesizer. The main challenge is to ensure that most appearances are visible, are allotted a similar pixel area, and are ordered in a smooth manner throughout the preview. Furthermore, to improve parameters selection we augment sliders controlling parameters with visual previews revealing the changes that will be introduced upon manipulation. Second, to allow user interactions with these visual previews we rely on a fast patch-based synthesizer. This synthesizer achieves a high degree of parallelism and is implemented entirely on the GPU. Finally, rather than generating the output of the synthesizer as a bitmap texture we encode the result in a compact representation and allow to decoding texels from this representation during rendering

Neste trabalho são apresentadas, complementadas e melhoradas duas técnicas de restauração de imagens: uma abordando o problema de retoque digital/remoção de objetos enquanto a segunda é direcionada ao problema deneliminação de ruído. Em ambas as técnicas, a ideia é trabalhar com imagens contendo texturas e outras características de interesse para um observador humano como a preservação de padrões, bordas, estruturas e regiões de natureza oscilatória. A técnica descrita sobre retoque digital de imagens combina difusão anisotrópica, síntese de texturas, busca dinâmica e um novo termo empregado no mecanismo de atribuição da ordem de prioridade durante o processo de reconstrução. Assim, dada uma imagem com regiões a serem recompostas, uma técnica de difusão anisotrópica é aplicada à imagem afim de se obter um mapa de saliência contendo bordas, estruturas e demais informações de baixa frequência da imagem. Na sequência, um mecanismo de prioridade baseado em um novo termo de confiabilidade regularizado é calculado a partir da combinação do mapa anteriormente gerado com a equação do transporte. Tal mecanismo é utilizado para determinar a ordem de preenchimento das partes faltantes da imagem. Para essa tarefa, a abordagem apresentada utiliza uma nova medida de similaridade entre blocos de pixels(amostrados dinamicamente para acelerar o processo), afim de encontrar os melhores candidatos a serem alocados nas regiões danificadas. A técnica destinada à remoção de ruídos alia a teoria da difusão anisotrópica, técnicas de análise harmônica e modelos numéricos de discretização de EDPs não-lineares em uma equação diferencial parcial regularizada, a qual atua de forma incisiva em regiões mais homogêneas da imagem e de forma mais suave em regiões caracterizadas como textura e bordas, preservando, assim, essas regiões. Além da natureza anisotrópica, a EDP procura recompor partes texturizadas perdidas no processo de eliminação de ruído através da aplicação de técnicas robustas de análise harmônica. Uma validação teórica e experimental para esta EDP e um estudo do ajuste paramétrico do método de eliminação de ruído baseado nesta EDP foram realizados neste trabalho. A eficiência e a performance das técnicas propostas são atestadas por meio das análises experimentais quantitativas e qualitativas com outras abordagens clássicas da literatura. / In this work two techniques of image restoration are presented, complemented and improved: one approaching the problem of image inpainting/object removal problem while the second one dealing with the image denoising problem. In both cases, the core idea is to process images containing textures and other features perceptible to a human observer such as patterns, contours, structures and oscillatory information. The image inpainting technique combines anisotropic diffusion, texture synthesis, dynamic search and a mechanism to set the order of priority during the image completion process. More precisely, given an image and target region to be inpainted, an anisotropic diffusion technique is applied in order to generate a saliency map containing edges, structures and other low frequency parts of the image. Next, apriority mechanism based on a new biased confidence term is computed from the map previously generated with the transport equation to define the level of priority of the pixels during the filling procedure. To accomplish this task, the presented approach employs a novel measure of similarity wich measures the distance between blocks of pixels (sampled dynamically to speed up the process) in order to find the best candidates to be allocated in the damaged regions. The technique devoted to denoising an image combines the theory of anisotropic diffusion, harmonic analysis techniques and numerical models into a regularized partial differential equation, which diffuses the pixels more incisively on homogeneous regions of the image while still seeking to attenuate regions formed by textures and patterns, thus preserving those information. Moreover, the proposed PDE aims at recovering texturized regions which have been degraded during the denoising process by employing harmonic analysis tools. A theoretical and experimental validation for this EDP and a study of the parametric adjustment of the image denoising method based on this EDP were performed in this work. The effectivenss and performance of the proposed approaches are attested through a comprehensive set of comparisons against other representative techniques in the literature.

As técnicas para síntese de textura em Computação Gráfica constituem um grupo bem específico cujo objetivo principal é a inclusão, na imagem, de alguma informação visual que aumente a percepção de realismo. São identificadas técnicas para síntese de textura com características favoráveis a implementação num ambiente limitado cujo aspecto central é a placa de vídeo VGA, a saber: mapeamento de textura, "bump mapping" e textura sólida. Um sistema para visualização de objetos simples com aplicação das técnicas selecionadas é descrito e implementado. Várias imagens são sintetizadas e os resultados analisados considerando-se o nível de realismo atingido. É explorado o uso de padrões para incremento da resolução visual das imagens em conjunto com a técnica de mapeamento de textura. Apresenta-se uma série de definições para textura das áreas de Computação Gráfica, Psicologia e Processamento de Imagens. Estas definições se integram e possibilitam a formação de um conceito genérico sobre o assunto. Os modelos e técnicas para descrição e síntese de texturas são apresentados, identificando-se as tendências nesta área. / The Computer Graphics texture synthesis techniques are a well defined group which main goal is to add visual information to the image. This visual information will increase the realism. Texture synthesis techniques for implementation in a limited environment are identified, namely texture mapping, bump mapping and solid texture. The main aspect of the environment is the VGA video card. A system is described and implemented to visualize simple objects where the selected texture synthesis techniques were applied . Some images are synthesized and the results are analysed. It is explored the use of patterns to increase images visual resolution in conection with texture mapping technique. It is presented a set of texture definitions from Computer Graphics, Psychology and Image Processing studies. These definitions are integrated and make possible to form a generic concept about the subject. Models and techniques for texture description and synthesis are presented. This survey identified trends in this area.

In this thesis we develop algorithms for the processing of textures and images using a ladder-based biorthogonal directional filter bank (DFB). This work is based on the DFB originally proposed by Bamberger and Smith. First we present a novel implementation of this filter bank using ladder structures. This new DFB provides non-trivial FIR perfect reconstruction systems which are computationally very efficient. Furthermore we address the lack of shift-invariance in the DFB by presenting a novel undecimated DFB that preserves the computational simplicity of its maximally decimated counterpart. Finally, we study the use of the DFB in combination with pyramidal structures to form polar-separable image decompositions. Using the proposed filter banks we develop and evaluate algorithms for texture classification, segmentation and synthesis. We perform a comparative study with other image representations and find that the DFB provides some of the best results reported on the data sets used. Using the proposed directional pyramids we adapt wavelet thresholding algorithms. We find that our decompositions provide better edge and contour preservation than the best results reported using the undecimated discrete wavelet transform. Finally, we apply the developed algorithms to the analysis and processing of synthetic aperture radar (SAR) imagery. SAR image analysis is impaired by the presence of speckle noise. Our first objective will be to study the removal of speckle to enhance the visual quality of the image. Additionally, we implement land cover segmentation and classification algorithms taking advantage of the textural characteristics of SAR images. Finally, we propose a model-based SAR image compression algorithm in which the speckle component is separated from the structural features of a scene. The speckle component is captured with a texture model and the scene component is coded with a wavelet coder at very low bit rates. The resulting decompressed images have a better perceptual quality than SAR images compressed without removing speckle.

Two-dimensional geometric textures are the geometric analogues of raster (pixel-based) textures and consist of planar distributions of discrete shapes with an inherent structure. These textures have many potential applications in art, computer graphics, and cartography. Synthesizing large textures by hand is generally a tedious task. In raster-based synthesis, many algorithms have been developed to limit the amount of manual effort required. These algorithms take in a small example as a reference and produce larger similar textures using a wide range of approaches. Recently, an increasing number of example-based geometric synthesis algorithms have been proposed. I refer to them in this dissertation as Geometric Texture Synthesis (GTS) algorithms. Analogous to their raster-based counterparts, GTS algorithms synthesize arrangements that ought to be judged by human viewers as “similar” to the example inputs. However, an absence of conventional evaluation procedures in current attempts demands an inquiry into the visual significance of synthesized results. In this dissertation, I present an investigation into GTS and report on my findings from three projects. I start by offering initial steps towards grounding texture synthesis techniques more firmly with our understanding of visual perception through two psychophysical studies. My observations throughout these studies result in important visual cues used by people when generating and/or comparing similarity of geometric arrangements as well a set of strategies adopted by participants when generating arrangements. Based on one of the generation strategies devised in these studies I develop a new geometric synthesis algorithm that uses a tile-based approach to generate arrangements. Textures synthesized by this algorithm are comparable to the state of the art in GTS and provide an additional reference in subsequent evaluations. To conduct effective evaluations of GTS, I start by collecting a set of representative examples, use them to acquire arrangements from multiple sources, and then gather them into a dataset that acts as a standard for the GTS research community. I then utilize this dataset in a second set of psychophysical studies that define an effective methodology for comparing current and future geometric synthesis algorithms.

[Truncated abstract] Texture exists all around us and serves as an important visual cue for the human visual system. Captured within an image, we identify texture by its recognisable visual pattern. It carries extensive information and plays an important role in our interpretation of a visual scene. The subject of this thesis is texture synthesis, which is de ned as the creation of a new texture that shares the fundamental visual characteristics of an existing texture such that the new image and the original are perceptually similar. Textures are used in computer graphics, computer-aided design, image processing and visualisation to produce realistic recreations of what we see in the world. For example, the texture on an object communicates its shape and surface properties in a 3D scene. Humans can discriminate between two textures and decide on their similarity in an instant, yet, achieving this algorithmically is not a simple process. Textures range in complexity and developing an approach that consistently synthe- sises this immense range is a dfficult problem to solve and motivates this research. Typically, texture synthesis methods aim to replicate texture by transferring the recognisable repeated patterns from the sample texture to synthesised output. Feature transferal can be achieved by matching pixels or patches from the sample to the output. As a result, two main approaches, pixel-based and patch-based, have es- tablished themselves in the active eld of texture synthesis. This thesis contributes to the present knowledge by introducing two novel texture synthesis methods. Both methods use image segmentation to improve synthesis results. ... The sample is segmented and the boundaries of the middle patch are confined to follow segment boundaries. This prevents texture features from being cut o prematurely, a common artifact of patch-based results, and eliminates the need for patch boundary comparisons that most other patch- based synthesis methods employ. Since no user input is required, this method is simple and straight-forward to run. The tiling of pre-computed tile pairs allows outputs that are relatively large to the sample size to be generated quickly. Output results show great success for textures with stochastic and semi-stochastic clustered features but future work is needed to suit more highly structured textures. Lastly these two texture synthesis methods are applied to the areas of image restoration and image replacement. These two areas of image processing involve replacing parts of an image with synthesised texture and are often referred to as constrained texture synthesis. Images can contain a large amount of complex information, therefore replacing parts of an image while maintaining image fidelity is a difficult problem to solve. The texture synthesis approaches and constrained synthesis implementations proposed in this thesis achieve successful results comparable with present methods.

As técnicas para síntese de textura em Computação Gráfica constituem um grupo bem específico cujo objetivo principal é a inclusão, na imagem, de alguma informação visual que aumente a percepção de realismo. São identificadas técnicas para síntese de textura com características favoráveis a implementação num ambiente limitado cujo aspecto central é a placa de vídeo VGA, a saber: mapeamento de textura, "bump mapping" e textura sólida. Um sistema para visualização de objetos simples com aplicação das técnicas selecionadas é descrito e implementado. Várias imagens são sintetizadas e os resultados analisados considerando-se o nível de realismo atingido. É explorado o uso de padrões para incremento da resolução visual das imagens em conjunto com a técnica de mapeamento de textura. Apresenta-se uma série de definições para textura das áreas de Computação Gráfica, Psicologia e Processamento de Imagens. Estas definições se integram e possibilitam a formação de um conceito genérico sobre o assunto. Os modelos e técnicas para descrição e síntese de texturas são apresentados, identificando-se as tendências nesta área. / The Computer Graphics texture synthesis techniques are a well defined group which main goal is to add visual information to the image. This visual information will increase the realism. Texture synthesis techniques for implementation in a limited environment are identified, namely texture mapping, bump mapping and solid texture. The main aspect of the environment is the VGA video card. A system is described and implemented to visualize simple objects where the selected texture synthesis techniques were applied . Some images are synthesized and the results are analysed. It is explored the use of patterns to increase images visual resolution in conection with texture mapping technique. It is presented a set of texture definitions from Computer Graphics, Psychology and Image Processing studies. These definitions are integrated and make possible to form a generic concept about the subject. Models and techniques for texture description and synthesis are presented. This survey identified trends in this area.

As técnicas para síntese de textura em Computação Gráfica constituem um grupo bem específico cujo objetivo principal é a inclusão, na imagem, de alguma informação visual que aumente a percepção de realismo. São identificadas técnicas para síntese de textura com características favoráveis a implementação num ambiente limitado cujo aspecto central é a placa de vídeo VGA, a saber: mapeamento de textura, "bump mapping" e textura sólida. Um sistema para visualização de objetos simples com aplicação das técnicas selecionadas é descrito e implementado. Várias imagens são sintetizadas e os resultados analisados considerando-se o nível de realismo atingido. É explorado o uso de padrões para incremento da resolução visual das imagens em conjunto com a técnica de mapeamento de textura. Apresenta-se uma série de definições para textura das áreas de Computação Gráfica, Psicologia e Processamento de Imagens. Estas definições se integram e possibilitam a formação de um conceito genérico sobre o assunto. Os modelos e técnicas para descrição e síntese de texturas são apresentados, identificando-se as tendências nesta área. / The Computer Graphics texture synthesis techniques are a well defined group which main goal is to add visual information to the image. This visual information will increase the realism. Texture synthesis techniques for implementation in a limited environment are identified, namely texture mapping, bump mapping and solid texture. The main aspect of the environment is the VGA video card. A system is described and implemented to visualize simple objects where the selected texture synthesis techniques were applied . Some images are synthesized and the results are analysed. It is explored the use of patterns to increase images visual resolution in conection with texture mapping technique. It is presented a set of texture definitions from Computer Graphics, Psychology and Image Processing studies. These definitions are integrated and make possible to form a generic concept about the subject. Models and techniques for texture description and synthesis are presented. This survey identified trends in this area.