Many lakes, coastal zones and oceans are directly or indirectly influenced by human activities. Through the outlet of a vast amount of substances in the air and water, we are changing the natural conditions on local and global levels. Remote sensing sensors, on satellites or airplanes, can collect image data, providing the user with information about the depicted area, object or phenomenon. Three different applications are discussed in this thesis. In the first part, we have used a bio-optical model to derive information about water quality parameters from remote sensing data collected over Swedish lakes. In the second part, remote sensing data have been used to locate and map wastewater plumes from pulp and paper industries along the east coast of Sweden. Finally, in the third part, we have investigated to what extent satellite data can be used to monitor coral reefs and detect coral bleaching. Regardless of application, it is important to understand the limitations of this technique. The available sensors are different and limited in terms of their spatial, spectral, radiometric and temporal resolution. We are also limited with respect to the objects we are monitoring, as the concentration of some substances is too low or the objects are too small, to be identified from space. However, this technique gives us a possibility to monitor our environment, in this case the aquatic environment, with a superior spatial coverage. Other advantages with remote sensing are the possibility of getting updated information and that the data is collected and distributed in digital form and therefore can be processed using computers.

Geotextile and geomembrane sheets are typically used as a composite system rather than as a stand-alone solution because of their complementary properties of permeability and stiffness. Previous researchers have focused on the large-scale interaction of fiber-texture interfaces while the micromechanical behavior of the internal geotextile structure has received limited attention. Characterizing the variation in the arrangement and distribution of filaments/voids is essential to understanding the micro-scale mechanisms of nonwoven fabrics interacting with counterface materials. This presentation summarizes the results from a study that examined the micromechanical mechanisms involved at needle-punched nonwoven geotextile-textured HDPE geomembrane interfaces and relates the results to the observed macro-scale response. A large displacement direct interface shear device was developed and used in this study to reduce the system errors that often occur with conventional shear devices and to allow internal geotextile strains to occur during shear. Complimentary numerical modeling was undertaken to study interface response. An advanced image analysis technique was applied to allow the evolution of the filament microstructure under various boundary and load conditions to be quantified. The different phases within the geosynthetic interface zone were detected from images captured using high-resolution optical microscopy. The changes of geotextile inner structures were statistically quantified in terms of inter-filament distance changes as well as the local void ratio and inscribing void size distributions. The tensile response of single filaments was measured using a helium neon deflectometer and these measurements were used to evaluate the shear induced filament strain. The study provides insight into the combined role of geomembrane surface topography and geotextile filament structure on macro-scale geosynthetic interface response.

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Thesis (Ph. D.)--Montana State University--Bozeman, 2007. / Typescript. Chairperson, Graduate Committee: Anne Camper. Includes bibliographical references (leaves 104-113).

Image analysis can provide genetic as well as protein level information from fluorescence stained fixed or living cells without loosing tissue morphology. Analysis of spatial, spectral, and temporal distribution of fluorescence can reveal important information on the single cell level. This is in contrast to most other methods for cell analysis, which do not account for inter-cellular variation. Flow cytometry enables single-cell analysis, but tissue morphology is lost in the process, and temporal events cannot be observed. The need for reproducibility, speed and accuracy calls for computerized methods for cell image analysis, i.e., digital image cytometry, which is the topic of this thesis. Algorithms for cell-based screening are presented and applied to evaluate the effect of insulin on translocation events in single cells. This type of algorithms could be the basis for high-throughput drug screening systems, and have been developed in close cooperation with biomedical industry. Image based studies of cell cycle proteins in cultured cells and tissue sections show that cyclin A has a well preserved expression pattern while the expression pattern of cyclin E is disturbed in tumors. The results indicate that analysis of cyclin E expression provides additional valuable information for cancer prognosis, not visible by standard tumor grading techniques. Complex chains of events and interactions can be visualized by simultaneous staining of different proteins involved in a process. A combination of image analysis and staining procedures that allow sequential staining and visualization of large numbers of different antigens in single cells is presented. Preliminary results show that at least six different antigens can be stained in the same set of cells. All image cytometry requires robust segmentation techniques. Clustered objects, background variation, as well as internal intensity variations complicate the segmentation of cells in tissue. Algorithms for segmentation of 2D and 3D images of cell nuclei in tissue by combining intensity, shape, and gradient information are presented. The algorithms and applications presented show that fast, robust, and automatic digital image cytometry can increase the throughput and power of image based single cell analysis.

Document image analysis involves the recognition and understanding of document images using computer vision techniques. The research described in this thesis relates to the recognition of graphical elements of a document image. More specifically, an approach for recognizing various types of charts as well as their components is presented. This research has many potential applications. For example, a user could redraw a chart in a different style or convert the chart to a table, without possessing the original information that was used to create the chart. Another application is the ability to find information, which is only presented in the chart, using a search engine. A complete solution to chart image recognition and understanding is presented. The proposed algorithm extracts enough information such that the chart can be recreated. The method is a syntactic approach which uses mathematical grammars to recognize and classify every component of a chart. There are two grammars presented in this thesis, one which analyzes 2D and 3D pie charts and the other which analyzes 2D and 3D bar charts, as well as line charts. The pie chart grammar isolates each slice and its properties whereas the bar and line chart grammar recognizes the bars, indices, gridlines and polylines. The method is evaluated in two ways. A qualitative approach redraws the chart for the user, and a semi-automated quantitative approach provides a complete analysis of the accuracy of the proposed method. The qualitative analysis allows the user to see exactly what has been classified correctly. The quantitative analysis gives more detailed information about the strengths and weaknesses of the proposed method. The results of the evaluation process show that the accuracy of the proposed methods for chart recognition is very high. / Graduate

Made available in DSpace on 2014-06-11T19:34:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-09-11Bitstream added on 2014-06-13T20:06:01Z : No. of bitstreams: 1 cruz_tg_dr_guara.pdf: 6855273 bytes, checksum: c4d4c4e4cbb6489a37fda09a1a0d409c (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Este trabalho propõe uma metodologia, baseada no processamento digital de imagens, para o monitoramento do processo de conformação cerâmica com amidos comerciais. No estudo da formação dos canais porosos e o processo de gelatinização foi utilizada a microscopia óptica a quente e análise do comportamento dos amidos com o aumento da temperatura. Para a caracterização dos poros durante as etapas da sua formação, foi utilizada a reconstrução por extensão de foco. Como resultado complementar às técnicas utilizadas foi desenvolvida uma metodologia, com base em tratamento estatístico, para caracterização espacial da porosidade. Foi feito o mapeamento das concentrações de porosidade e visualização em 3-D dessas regiões. / A methodology is proposed to establish standards for consolidation casting ceramics process with commercial starches, based on digital image processing. Hot stage light microscopy has been used to study porous channels formation and gelling process, evaluating starches behavior with temperature rising. Depth-from-focus reconstruction and quantitative microscopy have been applied to characterize pores during their formation. As an supplementary result, a new method for statistical based spatial characterization of porous three-dimensional distribution has been developed. It provides 3-D maps for visualization of porosities concentration distribution.

This paper provides a fully automated method of finding high-quality seed points in 3D space from a stack of images of neuron cells. These seed points may then be used as initial starting points for automated local tracing algorithms, removing a time consuming required user interaction in current methodologies. Methods to collapse the search space and provide rudimentary topology estimates are also presented. / Master of Science

This dissertation develops new approaches for improving the performance of hyperspectral target detection. Different aspects of hyperspectral target detection are reviewed and studied to effectively distinguish target features from background interference. The contributions of this dissertation are detailed as follows. 1) Propose an adaptive background characterization method that integrates region segmentation with target detection. In the experiments, not only unstructured matched filter based detectors are considered, but also two hybrid detectors combining fully constrained least squared abundance estimation with statistic test (i.e., adaptive matched subspace detector and adaptive cosine/coherent detector) are investigated. The experimental results demonstrate that using local adaptive background characterization, background clutters can be better suppressed than the original algorithms with global characterization. 2) Propose a new approach to estimate abundance fractions based on the linear spectral mixture model for hybrid structured and unstructured detectors. The new approach utilizes the sparseness constraint to estimate abundance fractions, and achieves better performance than the popular non-negative and fully constrained methods in the situations when background endmember spectra are not accurately acquired or estimated, which is very common in practical applications. To improve the dictionary incoherence, the use of band selection is proposed to improve the sparseness constrained linear unmixing. 3) Propose random projection based dimensionality reduction and decision fusion approach for detection improvement. Such a data independent dimensionality reduction process has very low computational cost, and it is capable of preserving the original data structure. Target detection can be robustly improved by decision fusion of multiple runs of random projection. A graphics processing unit (GPU) parallel implementation scheme is developed to expedite the overall process. 4) Propose nonlinear dimensionality reduction approaches for target detection. Auto-associative neural network-based Nonlinear Principal Component Analysis (NLPCA) and Kernel Principal Component Analysis (KPCA) are applied to the original data to extract principal components as features for target detection. The results show that NLPCA and KPCA can efficiently suppress trivial spectral variations, and perform better than the traditional linear version of PCA in target detection. Their performance may be even better than the directly kernelized detectors.