Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, since under- or over- estimation of this number may lead to incorrect unmixing for unsupervised methods. In this thesis we introduce a new method for determining the intrinsic dimension, using recent advances in Random Matrix Theory (RMT). This method is not sensitive to non-i.i.d. and correlated noise, and it is entirely unsupervised and free from any user-determined parameters. The new RMT method is mathematically derived, and robustness tests are run on synthetic data to determine how the results are a ected by: image size; noise levels; noise variability; noise approximation; spectral characteristics of the endmembers, etc. Success rates are determined for many di erent synthetic images, and the method is compared to two principal state of the art methods, Noise Subspace Projection (NSP) and HySime. All three methods are then tested on twelve real hyperspectral images, including images acquired by satellite, airborne and land-based sensors. When images that were acquired by di erent sensors over the same spatial area are evaluated, RMT gives consistent results, showing the robustness of this method to sensor characterisics.

Visual perception of images is closely related to the recognition of the different texture areas within an image. Identifying the boundaries of these regions is an important step in image analysis and image understanding. This thesis presents supervised and unsupervised methods which allow an efficient segmentation of the texture regions within multi-texture images. The features used by the methods are based on a measure of the fractal dimension of surfaces in several directions, which allows the transformation of the image into a set of feature images, however no direct measurement of the fractal dimension is made. Using this set of features, supervised and unsupervised, statistical processing schemes are presented which produce low classification error rates. Natural texture images are examined with particular application to the analysis of sonar images of the seabed. A number of processes based on fractal models for texture synthesis are also presented. These are used to produce realistic images of natural textures, again with particular reference to sonar images of the seabed, and which show the importance of phase and directionality in our perception of texture. A further extension is shown to give possible uses for image coding and object identification.

Thesis (Ph.D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 183-192)

Thesis (M.S. in Combat Systems Technology)--Naval Postgraduate School, December 2004. / Thesis advisor(s): Alfred W.Cooper, Gamani Karunasiri. Includes bibliographical references (p. 91-92). Also available online.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xv, 105 p. : ill. (some col.). Advisor: Jogikal Jagadeesh, Department of Electrical and Computer Engineering. Includes bibliographical references (p. 101-105).

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves [228]-237). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves [228]-237). Also available on the Internet.

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on Apr. 30, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 123 pages. Includes vita. Includes bibliographical references.

<p><b>Objective:</b></p><p>To develop and validate computer-assisted morphometrical methods, based on stereological theory, in order to facilitate the analysis and quantitative measurements of biological tissue components.</p><p><b>Material and methods:</b></p><p>Biopsy specimens from the vaginal wall or from the vestibulum vaginae of healthy women, or from women suffering from incontinence or vestibulitis were used.</p><p>A number of histochemical methods for light microscopy were used, and modified for the different morphometrical analyses. Electron microscopy was used to reveal collagen fibre diameter.</p><p>Computer-assisted morphometry, based on image analysis and stereology, was employed to analyse the different tissue components in the biopsies. Computer programs for these purposes were developed and validated.</p><p><b>Results:</b></p><p>The results show that computer-assisted morphometry is of great value for quantitative measurements of the following tissue components:</p><p><b>Epithelium:</b> The epithelial structure, instead of just thickness, was measured in an unbiased way.</p><p><b>Collagen:</b> The collagen fibril diameter was determined in electron microscopic specimens, and the collagen content was analysed in light microscopic specimens.</p><p><b>Elastic fibres:</b> The amount of elastic fibres in the connective tissue was measured after visualisation by autofluorescence.</p><p><b>Vasculature:</b> A stereological method using a cycloid grid was implemented in a computer program. Healthy subjects were compared with patients suffering from vestibulitis. The results were identical in the two groups.</p><p><b>Smooth muscle:</b> A stereological method using a point grid was implemented in a computer program. Using the Delesse principle, the fibres were calculated as area fractions. The area fractions were highly variable among the different specimens.</p><p><b>Conclusion:</b></p><p>Morphometry, used correctly, is an important analysis method in histopathological research. It is important that the methods are as simple and user-friendly as possible. The present studies show that this methodology can be applied for most quantitative histological analyses.</p>