This thesis summarizes a series of studies of how image quality is affected by the choice of sampling pattern in 3D. Our comparison includes the Cartesian cubic (CC) lattice, the body-centered cubic (BCC) lattice, and the face-centered cubic (FCC) lattice. Our studies of the lattice Brillouin zones of lattices of equal density show that, while the CC lattice is suitable for functions with elongated spectra, the FCC lattice offers the least variation in resolution with respect to direction. The BCC lattice, however, offers the highest global cutoff frequency. The difference in behavior between the BCC and FCC lattices is negligible for a natural spectrum. We also present a study of pre-aliasing errors on anisotropic versions of the CC, BCC, and FCC sampling lattices, revealing that the optimal choice of sampling lattice is highly dependent on lattice orientation and anisotropy. We suggest a new reference function for studies of aliasing errors on alternative sampling lattices. This function has a spherical spectrum, and a frequency content proportional to the distance from the origin, facilitating studies of pre-aliasing in spatial domain. The accuracy of anti-aliased Euclidean distance transform is improved by application of more sofisticated methods for computing the sub-spel precision term. We find that both accuracy and precision are higher on the BCC and FCC lattices than on the CC lattice. We compare the performance of several intensity-weighted distance transforms on MRI data, and find that the derived segmentation result, with respect to relative error in segmented volume, depends neither on the sampling lattice, nor on the sampling density. Lastly, we present LatticeLibrary, a open source C++ library for processing of sampled data, supporting a number of common image processing methods for CC, BCC, and FCC lattices. We also introduce BccFccRaycaster, a tool for visualizing data sampled on CC, BCC, and FCC lattices. We believe that the work summarized in this thesis provide both the motivation and the tools for continuing research on application of the BCC and FCC lattices in image processing and analysis.

Geometric shapes can be represented in a variety of different ways. A distancemap is a map from points to distances. This can be used as a shape representationwhich can be created through a process known as a distance transform. This the-sis project tests a method for three-dimensional distance transforms using frac-tional volume coverage. This method produces distance maps with subvoxel dis-tance values. The result which is achieved is clearly better than what would beexpected from a binary distance transform and similar to the one known fromprevious work. The resulting code has been published under a free and opensource software license. / <p>The developed code is available under a GPL license here https://gitlab.com/Emiluren/3d-distance-transform</p>

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Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzir o tempo de execução da TDE. Dois algoritmos de TDE são paralelizados. O primeiro, um algoritmo de TDE por varredura independente, é paralelizado em um SMP e em um agregado. O segundo, um algoritmo de TDE por propagação ordenada, é paralelizado no agregado. / The Euclidean distance transform is the operation that converts a binary image made of object and background pixels into another image, the Euclidean distance map, where each pixel has a value corresponding to the Euclidean distance from this pixel to the background. The Euclidean distance transform has important uses in computer vision, image analysis and robotics, but it is time-consuming, mainly when processing 3-D images. In this work two types of parallel computers are used to speed up the Euclidean distance transform, (i) symmetric multiprocessors (SMPs) and (ii) clusters of workstations. Two algorithms are parallelized. The first one, an independent line-column Euclidean distance transform algorithm, is parallelized on a SMP, and on a cluster. The second one, an ordered propagation Euclidean distance transform algorithm, is paralellized on a cluster.

Transformada de distância euclidiana (TDE) é a operação que converte uma imagem binária composta de pontos de objeto e de fundo em outra, chamada mapa de distâncias euclidianas, onde o valor armazenado em cada ponto corresponde à menor distância euclidiana entre este ponto e o fundo da imagem. A TDE é muito utilizada em visão computacional, análise de imagens e robótica, mas é uma transformação muito demorada, principalmente em imagens 3-D. Neste trabalho são utilizados dois tipos de computadores paralelos, (i) multiprocessadores simétricos (SMPs) e (ii) agregados de computadores, para reduzir o tempo de execução da TDE. Dois algoritmos de TDE são paralelizados. O primeiro, um algoritmo de TDE por varredura independente, é paralelizado em um SMP e em um agregado. O segundo, um algoritmo de TDE por propagação ordenada, é paralelizado no agregado. / The Euclidean distance transform is the operation that converts a binary image made of object and background pixels into another image, the Euclidean distance map, where each pixel has a value corresponding to the Euclidean distance from this pixel to the background. The Euclidean distance transform has important uses in computer vision, image analysis and robotics, but it is time-consuming, mainly when processing 3-D images. In this work two types of parallel computers are used to speed up the Euclidean distance transform, (i) symmetric multiprocessors (SMPs) and (ii) clusters of workstations. Two algorithms are parallelized. The first one, an independent line-column Euclidean distance transform algorithm, is parallelized on a SMP, and on a cluster. The second one, an ordered propagation Euclidean distance transform algorithm, is paralellized on a cluster.

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions.

A Transformada Distância é uma importante ferramenta para o processamento de imagens digitais. A partir dela podemos calcular a dimensão fractal e obter o esqueleto de um objeto. Estas operações são muito importantes na análise de formas e no reconhecimento de padrões. Porém poucas são as implementações em hardware específico para este processamento. Neste trabalho apresentamos a implementação de arquiteturas paralelas para a determinação da Transformada Distância e para a geração de esqueletos baseados no algoritmo de Dilatação Exata e Propagação de Rótulos. Propomos também uma implementação do algoritmo utilizando a biblioteca MPI para o processamento paralelo. / Distance Transform is an important tool for digital imaging processing. Using the Distance Transform it is possible to evaluate the fractal dimension and skeletons of objects. Fractal dimension and skeletons are very important in shape analysis and pattern recognition operations, few are the hardware implementation for these operations. In this work we present a parallel implementation based on the exact dilation and label propagation for the evaluation of the Distance Transform and skeletons generation. We also propose an algorithm implementation using the MPI library for parallel processing.

The overall aim of my PhD research work is to design, develop, and evaluate a new practical environment to generate separated representations of arterial and venous trees in non-contrast pulmonary CT imaging of human subjects and to extract quantitative measures at different tree-levels. Artery/vein (A/V) separation is of substantial importance contributing to our understanding of pulmonary structure and function, and immediate clinical applications exist, e.g., for assessment of pulmonary emboli. Separated A/V trees may also significantly boost performance of airway segmentation methods for higher tree generations. Although, non-contrast pulmonary CT imaging successfully captures higher tree generations of vasculature, A/V are indistinguishable by their intensity values, and often, there is no trace of intensity variation at locations of fused arteries and veins. Patient-specific structural abnormalities of vascular trees further complicate the task. We developed a novel multi-scale topo-morphologic opening algorithm to separate A/V trees in non-contrast CT images. The algorithm combines fuzzy distance transform, a morphologic feature, with a topologic connectivity and a new morphological reconstruction step to iteratively open multi-scale fusions starting at large scales and progressing towards smaller scales. The algorithm has been successfully applied on fuzzy vessel segmentation results using interactive seed selection via an efficient graphical user interface developed as a part of my PhD project. Accuracy, reproducibility and efficiency of the system are quantitatively evaluated using computer-generated and physical phantoms along with in vivo animal and human data sets and the experimental results formed are quite encouraging. Also, we developed an arc-skeleton based volumetric tree generation algorithm to generate multi-level volumetric tree representation of isolated arterial/venous tree and to extract vascular measurements at different tree levels. The method has been applied on several computer generated phantoms and CT images of pulmonary vessel cast and in vivo pulmonary CT images of a pig at different airway pressure. Experimental results have shown that the method is quite accurate and reproducible. Finally, we developed a new pulmonary vessel segmentation algorithm, i.e., a new anisotropic constrained region growing method that encourages axial region growing while arresting cross-structure leaking. The region growing is locally controlled by tensor scale and structure scale and anisotropy. The method has been successfully applied on several non-contrast pulmonary CT images of human subjects. The accuracy of the new method has been evaluated using manually selection of vascular and non-vascular voxels and the results found are very promising.

A Transformada Distância é uma importante ferramenta para o processamento de imagens digitais. A partir dela podemos calcular a dimensão fractal e obter o esqueleto de um objeto. Estas operações são muito importantes na análise de formas e no reconhecimento de padrões. Porém poucas são as implementações em hardware específico para este processamento. Neste trabalho apresentamos a implementação de arquiteturas paralelas para a determinação da Transformada Distância e para a geração de esqueletos baseados no algoritmo de Dilatação Exata e Propagação de Rótulos. Propomos também uma implementação do algoritmo utilizando a biblioteca MPI para o processamento paralelo. / Distance Transform is an important tool for digital imaging processing. Using the Distance Transform it is possible to evaluate the fractal dimension and skeletons of objects. Fractal dimension and skeletons are very important in shape analysis and pattern recognition operations, few are the hardware implementation for these operations. In this work we present a parallel implementation based on the exact dilation and label propagation for the evaluation of the Distance Transform and skeletons generation. We also propose an algorithm implementation using the MPI library for parallel processing.

Distance field text rendering has many advantages compared to most other text renderingsolutions. Two of the advantages are the possibility  to scale the glyphs without losing the crisp edge and less memory consumption. A drawback with distance field text renderingcan be high distance field generation time. The solution for fast distance field text renderingin this thesis generates the distance fields by drawing distance gradients locally over the outlines of the glyphs. This method is much faster than the old exact methods for generating distance fields that often includes multiple passes over the whole image. Using the solution for text rendering proposed in this thesis results in good looking text that is generated on the fly. The distance fields are generated on a mobile device in less than 10 ms for most of the glyphs in good quality which is less than the time between two frames.