Um ambiente de data warehousing oferece suporte ao processo de tomada de decisão. Ele consolida dados de fontes de informação distribuições, autônomas e heterogêneas em um único componente, o data warehouse, e realiza o processamento eficiente de consultas analíticas, denominadas OLAP (on-line analytical processing). Um data warehouse convencional armazena apenas dados alfanuméricos. Por outro lado, um data warehouse de imagens armazena, além desses dados convencionais, características intrínsecas de imagens, permitindo a realização de consultas analíticas estendidas com predicados de similaridade entre imagens. Esses ambientes demandam, portanto, a criação de estratégias que possibilitem o processamento eficiente dessas consultas complexas e custosas. Apesar de haver na literatura trabalhos voltados a índices bitmap para ambientes de data warehousing e métodos de acesso métricos para melhorar o desempenho de consultas por similaridade entre imagens, no melhor do nosso conhecimento, não há uma técnica que investigue essas duas questões em um mesmo contexto. Esta dissertação visa preencher essa lacuna na literatura por meio das seguintes contribuições: (i) proposta do ImageDWindex, um mecanismo para a otimização de consultas analíticas estendidas com predicados de similaridade entre imagens; e (ii) definição de diferentes estratégias de processamento de consultas sobre data warehouses de imagens usando o ImageDW-index. Para validar as soluções propostas, foram desenvolvidas duas outras contribuições secundárias, que são: (iii) o ImageDW-Gen, um gerador de dados com o objetivo de povoar o data warehouse de imagens; e (iv) a proposta de quatro classes de consulta, as quais enfocam em diferentes custos de processamento dos predicados de similaridade entre imagens. Utilizando o ImageDW-Gen, foram realizados testes de desempenho para investigar as vantagens introduzidas pelas estratégias propostas, de acordo com as classes de consultas definidas. Comparado com o trabalho mais correlato existente na literatura, o uso do ImageDWindex proveu uma melhora no desempenho do processamento de consultas IOLAP que variou em média de 55,57% até 82,16%, considerando uma das estratégias propostas. / A data warehousing environment offers support to the decision-making process. It consolidates data from distributed, autonomous and heterogeneous information sources into one of its main components, the data warehouse. Furthermore, it provides effcient processing of analytical queries (i.e. OLAP queries). A conventional data warehouse stores only alphanumeric data. On the other hand, an image data warehouse stores not only alphanumeric data but also intrinsic features of images, thus allowing data warehousing environments to perform analytical similarity queries over images. This requires the development of strategies to provide efficient processing of these complex and costly queries. Although there are a number of approaches in the literature aimed at the development of bitmap index for data warehouses and metric access methods for the efficient processing of similarity queries over images, to the best of our knowledge, there is not an approach that investigate these two issues in the same setting. In this research, we fill this gap in the literature by introducing the following main contributions: (i) the proposal of the ImageDW-index, an optimization mechanism aimed at the efficient processing of analytical queries extended with similarity predicates over images; and (ii) definition of different processing strategies for image data warehouses using the ImageDW-index. In order to validate these main proposals, we also introduce two secondary contributions, as follows: (iii) the ImageDW-Gen, a data generator to populate image data warehouses; and (iv) the proposal of four query classes, each one enforcing different query processing costs associated to the similarity predicates in image data warehousing environments. Using the ImageDW-Gen, performance tests were carried out in order to investigate the advantages introduced by the proposed strategies, according to the query classes. Compared to the most related work available in the literature, the ImageDW-index provided a performance gain that varied from 55.57% to 82.16%, considering one of the proposed strategies.

This work introduces novel internal and external memory algorithms for computing voxel skeletons of massive voxel objects with complex network-like architecture and for converting these voxel skeletons to piecewise linear geometry, that is triangle meshes and piecewise straight lines. The presented techniques help to tackle the challenge of visualizing and analyzing 3d images of increasing size and complexity, which are becoming more and more important in, for example, biological and medical research. Section 2.3.1 contributes to the theoretical foundations of thinning algorithms with a discussion of homotopic thinning in the grid cell model. The grid cell model explicitly represents a cell complex built of faces, edges, and vertices shared between voxels. A characterization of pairs of cells to be deleted is much simpler than characterizations of simple voxels were before. The grid cell model resolves topologically unclear voxel configurations at junctions and locked voxel configurations causing, for example, interior voxels in sets of non-simple voxels. A general conclusion is that the grid cell model is superior to indecomposable voxels for algorithms that need detailed control of topology. Section 2.3.2 introduces a noise-insensitive measure based on the geodesic distance along the boundary to compute two-dimensional skeletons. The measure is able to retain thin object structures if they are geometrically important while ignoring noise on the object's boundary. This combination of properties is not known of other measures. The measure is also used to guide erosion in a thinning process from the boundary towards lines centered within plate-like structures. Geodesic distance based quantities seem to be well suited to robustly identify one- and two-dimensional skeletons. Chapter 6 applies the method to visualization of bone micro-architecture. Chapter 3 describes a novel geometry generation scheme for representing voxel skeletons, which retracts voxel skeletons to piecewise linear geometry per dual cube. The generated triangle meshes and graphs provide a link to geometry processing and efficient rendering of voxel skeletons. The scheme creates non-closed surfaces with boundaries, which contain fewer triangles than a representation of voxel skeletons using closed surfaces like small cubes or iso-surfaces. A conclusion is that thinking specifically about voxel skeleton configurations instead of generic voxel configurations helps to deal with the topological implications. The geometry generation is one foundation of the applications presented in Chapter 6. Chapter 5 presents a novel external memory algorithm for distance ordered homotopic thinning. The presented method extends known algorithms for computing chamfer distance transformations and thinning to execute I/O-efficiently when input is larger than the available main memory. The applied block-wise decomposition schemes are quite simple. Yet it was necessary to carefully analyze effects of block boundaries to devise globally correct external memory variants of known algorithms. In general, doing so is superior to naive block-wise processing ignoring boundary effects. Chapter 6 applies the algorithms in a novel method based on confocal microscopy for quantitative study of micro-vascular networks in the field of microcirculation. / Die vorliegende Arbeit führt I/O-effiziente Algorithmen und Standard-Algorithmen zur Berechnung von Voxel-Skeletten aus großen Voxel-Objekten mit komplexer, netzwerkartiger Struktur und zur Umwandlung solcher Voxel-Skelette in stückweise-lineare Geometrie ein. Die vorgestellten Techniken werden zur Visualisierung und Analyse komplexer drei-dimensionaler Bilddaten, beispielsweise aus Biologie und Medizin, eingesetzt. Abschnitt 2.3.1 leistet mit der Diskussion von topologischem Thinning im Grid-Cell-Modell einen Beitrag zu den theoretischen Grundlagen von Thinning-Algorithmen. Im Grid-Cell-Modell wird ein Voxel-Objekt als Zellkomplex dargestellt, der aus den Ecken, Kanten, Flächen und den eingeschlossenen Volumina der Voxel gebildet wird. Topologisch unklare Situationen an Verzweigungen und blockierte Voxel-Kombinationen werden aufgelöst. Die Charakterisierung von Zellpaaren, die im Thinning-Prozess entfernt werden dürfen, ist einfacher als bekannte Charakterisierungen von so genannten "Simple Voxels". Eine wesentliche Schlussfolgerung ist, dass das Grid-Cell-Modell atomaren Voxeln überlegen ist, wenn Algorithmen detaillierte Kontrolle über Topologie benötigen. Abschnitt 2.3.2 präsentiert ein rauschunempfindliches Maß, das den geodätischen Abstand entlang der Oberfläche verwendet, um zweidimensionale Skelette zu berechnen, welche dünne, aber geometrisch bedeutsame, Strukturen des Objekts rauschunempfindlich abbilden. Das Maß wird im weiteren mit Thinning kombiniert, um die Erosion von Voxeln auf Linien zuzusteuern, die zentriert in plattenförmigen Strukturen liegen. Maße, die auf dem geodätischen Abstand aufbauen, scheinen sehr geeignet zu sein, um ein- und zwei-dimensionale Skelette bei vorhandenem Rauschen zu identifizieren. Eine theoretische Begründung für diese Beobachtung steht noch aus. In Abschnitt 6 werden die diskutierten Methoden zur Visualisierung von Knochenfeinstruktur eingesetzt. Abschnitt 3 beschreibt eine Methode, um Voxel-Skelette durch kontrollierte Retraktion in eine stückweise-lineare geometrische Darstellung umzuwandeln, die als Eingabe für Geometrieverarbeitung und effizientes Rendering von Voxel-Skeletten dient. Es zeigt sich, dass eine detaillierte Betrachtung der topologischen Eigenschaften eines Voxel-Skeletts einer Betrachtung von allgemeinen Voxel-Konfigurationen für die Umwandlung zu einer geometrischen Darstellung überlegen ist. Die diskutierte Methode bildet die Grundlage für die Anwendungen, die in Abschnitt 6 diskutiert werden. Abschnitt 5 führt einen I/O-effizienten Algorithmus für Thinning ein. Die vorgestellte Methode erweitert bekannte Algorithmen zur Berechung von Chamfer-Distanztransformationen und Thinning so, dass diese effizient ausführbar sind, wenn die Eingabedaten den verfügbaren Hauptspeicher übersteigen. Der Einfluss der Blockgrenzen auf die Algorithmen wurde analysiert, um global korrekte Ergebnisse sicherzustellen. Eine detaillierte Analyse ist einer naiven Zerlegung, die die Einflüsse von Blockgrenzen vernachlässigt, überlegen. In Abschnitt 6 wird, aufbauend auf den I/O-effizienten Algorithmen, ein Verfahren zur quantitativen Analyse von Mikrogefäßnetzwerken diskutiert.

A determinação das propriedades mecânicas em materiais hiperelásticos constitui uma atividade complexa, a qual requer a realização de ensaios mecânicos em laboratório, seleção de modelos matemáticos adequados a cada tipo de comportamento e ajuste de curva sobre os dados experimentais. O ensaio biaxial por inflação se caracteriza pela inflação de uma membrana fina após a aplicação de uma pressão uniforme agindo na direção normal a superfície e tem sido utilizado para obter a curva tensão versus deformação. O objetivo do presente trabalho é estudá-lo como um teste para a caracterização mecânicas de borrachas incompressíveis. É proposta uma metodologia e aspectos como a aplicabilidade da teoria de cascas finas, geometria esférica no topo da amostra, instabilidade e processo de deformação são investigados. Um código computacional foi desenvolvido para identificação de parâmetros da amostra deformada através de aquisição de imagem. A validação da metodologia foi realizada tanto através de análises numéricas por elementos finitos como através de ensaios experimentais realizados em laboratório. Um novo método analítico para a determincação da deformação no topo membrana inflada (ou bolha) foi proposto e testado. / The determination of mechanical properties of hyperelastic materials is a complex activity, which requires mechanical laboratory tests to obtain the stress-strain curve, selection of the appropriate mathematical model and curve fitting of the experimental data. The biaxial inflation test is characterized by the inflation of a thin membrane by applying uniform pressure acting on the normal direction of the surface. The purpose of this paper is to study it as a test for characterization of incompressible rubberlike materials. A methodology is proposed and aspects such as the applicability of the theory of thin shell, spherical geometry on top of the sample, instability and kinematic deformation process are investigated. A computer code was developed for identification of parameters of the deformed specimen by image acquisition. The validation of the methodology was carried out through both numerical analysis by finite elements and through experimental testing performed in laboratory. A new analytic method to determine the deformation of the apex of the inflated membrane (or bubble) is proposed and tested.

A determinação das propriedades mecânicas em materiais hiperelásticos constitui uma atividade complexa, a qual requer a realização de ensaios mecânicos em laboratório, seleção de modelos matemáticos adequados a cada tipo de comportamento e ajuste de curva sobre os dados experimentais. O ensaio biaxial por inflação se caracteriza pela inflação de uma membrana fina após a aplicação de uma pressão uniforme agindo na direção normal a superfície e tem sido utilizado para obter a curva tensão versus deformação. O objetivo do presente trabalho é estudá-lo como um teste para a caracterização mecânicas de borrachas incompressíveis. É proposta uma metodologia e aspectos como a aplicabilidade da teoria de cascas finas, geometria esférica no topo da amostra, instabilidade e processo de deformação são investigados. Um código computacional foi desenvolvido para identificação de parâmetros da amostra deformada através de aquisição de imagem. A validação da metodologia foi realizada tanto através de análises numéricas por elementos finitos como através de ensaios experimentais realizados em laboratório. Um novo método analítico para a determincação da deformação no topo membrana inflada (ou bolha) foi proposto e testado. / The determination of mechanical properties of hyperelastic materials is a complex activity, which requires mechanical laboratory tests to obtain the stress-strain curve, selection of the appropriate mathematical model and curve fitting of the experimental data. The biaxial inflation test is characterized by the inflation of a thin membrane by applying uniform pressure acting on the normal direction of the surface. The purpose of this paper is to study it as a test for characterization of incompressible rubberlike materials. A methodology is proposed and aspects such as the applicability of the theory of thin shell, spherical geometry on top of the sample, instability and kinematic deformation process are investigated. A computer code was developed for identification of parameters of the deformed specimen by image acquisition. The validation of the methodology was carried out through both numerical analysis by finite elements and through experimental testing performed in laboratory. A new analytic method to determine the deformation of the apex of the inflated membrane (or bubble) is proposed and tested.

A determinação das propriedades mecânicas em materiais hiperelásticos constitui uma atividade complexa, a qual requer a realização de ensaios mecânicos em laboratório, seleção de modelos matemáticos adequados a cada tipo de comportamento e ajuste de curva sobre os dados experimentais. O ensaio biaxial por inflação se caracteriza pela inflação de uma membrana fina após a aplicação de uma pressão uniforme agindo na direção normal a superfície e tem sido utilizado para obter a curva tensão versus deformação. O objetivo do presente trabalho é estudá-lo como um teste para a caracterização mecânicas de borrachas incompressíveis. É proposta uma metodologia e aspectos como a aplicabilidade da teoria de cascas finas, geometria esférica no topo da amostra, instabilidade e processo de deformação são investigados. Um código computacional foi desenvolvido para identificação de parâmetros da amostra deformada através de aquisição de imagem. A validação da metodologia foi realizada tanto através de análises numéricas por elementos finitos como através de ensaios experimentais realizados em laboratório. Um novo método analítico para a determincação da deformação no topo membrana inflada (ou bolha) foi proposto e testado. / The determination of mechanical properties of hyperelastic materials is a complex activity, which requires mechanical laboratory tests to obtain the stress-strain curve, selection of the appropriate mathematical model and curve fitting of the experimental data. The biaxial inflation test is characterized by the inflation of a thin membrane by applying uniform pressure acting on the normal direction of the surface. The purpose of this paper is to study it as a test for characterization of incompressible rubberlike materials. A methodology is proposed and aspects such as the applicability of the theory of thin shell, spherical geometry on top of the sample, instability and kinematic deformation process are investigated. A computer code was developed for identification of parameters of the deformed specimen by image acquisition. The validation of the methodology was carried out through both numerical analysis by finite elements and through experimental testing performed in laboratory. A new analytic method to determine the deformation of the apex of the inflated membrane (or bubble) is proposed and tested.

Um ambiente de data warehousing oferece suporte ao processo de tomada de decisão. Ele consolida dados de fontes de informação distribuições, autônomas e heterogêneas em um único componente, o data warehouse, e realiza o processamento eficiente de consultas analíticas, denominadas OLAP (on-line analytical processing). Um data warehouse convencional armazena apenas dados alfanuméricos. Por outro lado, um data warehouse de imagens armazena, além desses dados convencionais, características intrínsecas de imagens, permitindo a realização de consultas analíticas estendidas com predicados de similaridade entre imagens. Esses ambientes demandam, portanto, a criação de estratégias que possibilitem o processamento eficiente dessas consultas complexas e custosas. Apesar de haver na literatura trabalhos voltados a índices bitmap para ambientes de data warehousing e métodos de acesso métricos para melhorar o desempenho de consultas por similaridade entre imagens, no melhor do nosso conhecimento, não há uma técnica que investigue essas duas questões em um mesmo contexto. Esta dissertação visa preencher essa lacuna na literatura por meio das seguintes contribuições: (i) proposta do ImageDWindex, um mecanismo para a otimização de consultas analíticas estendidas com predicados de similaridade entre imagens; e (ii) definição de diferentes estratégias de processamento de consultas sobre data warehouses de imagens usando o ImageDW-index. Para validar as soluções propostas, foram desenvolvidas duas outras contribuições secundárias, que são: (iii) o ImageDW-Gen, um gerador de dados com o objetivo de povoar o data warehouse de imagens; e (iv) a proposta de quatro classes de consulta, as quais enfocam em diferentes custos de processamento dos predicados de similaridade entre imagens. Utilizando o ImageDW-Gen, foram realizados testes de desempenho para investigar as vantagens introduzidas pelas estratégias propostas, de acordo com as classes de consultas definidas. Comparado com o trabalho mais correlato existente na literatura, o uso do ImageDWindex proveu uma melhora no desempenho do processamento de consultas IOLAP que variou em média de 55,57% até 82,16%, considerando uma das estratégias propostas. / A data warehousing environment offers support to the decision-making process. It consolidates data from distributed, autonomous and heterogeneous information sources into one of its main components, the data warehouse. Furthermore, it provides effcient processing of analytical queries (i.e. OLAP queries). A conventional data warehouse stores only alphanumeric data. On the other hand, an image data warehouse stores not only alphanumeric data but also intrinsic features of images, thus allowing data warehousing environments to perform analytical similarity queries over images. This requires the development of strategies to provide efficient processing of these complex and costly queries. Although there are a number of approaches in the literature aimed at the development of bitmap index for data warehouses and metric access methods for the efficient processing of similarity queries over images, to the best of our knowledge, there is not an approach that investigate these two issues in the same setting. In this research, we fill this gap in the literature by introducing the following main contributions: (i) the proposal of the ImageDW-index, an optimization mechanism aimed at the efficient processing of analytical queries extended with similarity predicates over images; and (ii) definition of different processing strategies for image data warehouses using the ImageDW-index. In order to validate these main proposals, we also introduce two secondary contributions, as follows: (iii) the ImageDW-Gen, a data generator to populate image data warehouses; and (iv) the proposal of four query classes, each one enforcing different query processing costs associated to the similarity predicates in image data warehousing environments. Using the ImageDW-Gen, performance tests were carried out in order to investigate the advantages introduced by the proposed strategies, according to the query classes. Compared to the most related work available in the literature, the ImageDW-index provided a performance gain that varied from 55.57% to 82.16%, considering one of the proposed strategies.

The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.

Development of selected invasive species and meadow vegetation classification algorithm in the Krkonoše Mountains using hyperspectral data Abstract The thesis deals with utilization of airbone APEX hyperspectral image data for selected invasive species and meadow vegetation classification in the study area of the Krkonoše Mountains National Park. The mian goal of the thesis was to develop of classification algorithm based on proposed vegetation indices. The approach was based on the utilization of in-situ LAI, fAPAR, chlorophyll content data and analysis of their relation with vegetation spectral properties. The work also deals with several problems regarding LAI - vegetation indices relationship, namely saturation of LAI and mutual correlation of LAI and chlorophyll content. Tha classification was focued on invasive species Rumex alpinus and Lupinus polyphyllus, meadow vegetation with dominant Nardus stricta and dominant Trisetum flavescens and cutted lawns. Besides the proposed approach, the presented work resulted in several classification maps of study area and in spectral libraries, containing ground level spectra of studied invasive species, meadow vegetation types and several other meadow species. Keywords: hyperspectral image data, APEX, LAI, fAPAR, vegetation indices, invasive species, meadow...

To prevent further brain tumour growth, malignant tissue should be removed as completely as possible in neurosurgical operations. Therefore, differentiation between tumour and brain tissue as well as detecting functional areas is very important. Hyperspectral imaging (HSI) can be used to get spatial information about brain tissue types and characteristics in a quasi-continuous reflection spectrum. In this paper, workflow and some aspects of an adapted hardware system for intraoperative hyperspectral data acquisition in neurosurgery are discussed. By comparing an intraoperative with a laboratory setup, the influences of the surgical microscope are made visible through the differences in illumination and a pixel- and wavelength-specific signal-to-noise ratio (SNR) calculation. Due to the significant differences in shape and wavelength-dependent intensity of light sources, it can be shown which kind of illumination is most suitable for the setups. Spectra between 550 and 1,000 nm are characterized of at least 40 dB SNR in laboratory and 25 dB in intraoperative setup in an area of the image relevant for evaluation. A first validation of the intraoperative hyperspectral imaging hardware setup shows that all system parts and intraoperatively recorded data can be evaluated. Exemplarily, a classification map was generated that allows visualization of measured properties of raw data. The results reveal that it is possible and beneficial to use HSI for wavelength-related intraoperative data acquisition in neurosurgery. There are still technical facts to optimize for raw data detection prior to adapting image processing algorithms to specify tissue quality and function.:Abstract Introduction Materials and methods (Clinical workflow and setup for hyperspectral imaging process, Characteristics of the lighting, Characteristics of the hyperspectral imaging camera, Spectral data acquisition and raw data pre-processing in neurosurgery, Spectral data evaluation) Results (Spectral characteristics of the lighting, SNR of the HSI camera, Data acquisition and raw data preprocessing during neurosurgical operation, Spectral data evaluation) Discussion Conclusions

Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas. / Ph. D.