Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains ix, 80 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 76-80).

Thesis (Ph. D.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains xiv, 195 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 181-195).

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains ix, 83 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 77-83).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains ix, 89 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 84-89).

Thesis (M.Sc.)--University of Alberta, 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 in Molecular Biology and Genetics, Department of Biological Sciences. Title from pdf file main screen (viewed on April 28, 2010). Accompanied by four supplementary video recording files. Includes bibliographical references.

Thesis (M.S. in Computer Science)--Naval Postgraduate School, 2008. / Title from PDF title screen. "March 2008." "ADA 479955." Includes bibliographical references (p. 109-113). Also available online.

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains ix, 50 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 48-50).

Image categorization is often performed manually, which can be a time consuming and a very difficult process, especially for human iris images. Previous researchers have been working on predicting ethnicity from texture features of iris images using other methods. This thesis is one of the the first to present a solution of iris image categorization using artificial neural networks, specifically for human iris images with discernible and complicated textures. The work will allow users to quickly and automatically categorize human iris images by using supervised and unsupervised learning algorithms. Contributions of this solution include a fast and accurate way to apply iris matching and solve the time consuming problems. The solution aims to find efficient and appropriate artificial neural network algorithms that can categorize iris images based on texture features. Detailed algorithms, specific techniques, performance analysis, limitations and future work will be also provided in this thesis.