Thesis (M. Med. Sc.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 40-46).

Zugl.: Regensburg, Universiẗat, Diss., 2009.

Zugl.: Siegen, Univ., Diss., 2009

Title from PDF of title page (University of Missouri--Columbia, viewed on March 10, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Jianlin Cheng. Includes bibliographical references.

Thesis (Ph. D.)--University of Queensland, 2003. / Includes bibliographical references.

Thesis (doctoral)--Universität St. Gallen, 2003.

Zugl.: München, Univ. der Bundeswehr, Diss., 2009

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Microbiology / Master / Master of Medical Sciences

This thesis is concerned with the utilisation of spatial information in processing of high-frequency sidescan SONAR imagery, and particularly in how such information can be used in developing techniques to assist in mapping functions. Survey applications aim to generate maps of the seabed, but are time consuming and expensive; automatic processing is required to improve efficiency. Current techniques have had some success, but utilise little of the available spatial information. Previously, inclusion of such knowledge was prohibitively expensive; recent improvements in numerical simulations techniques has reduced the costs involved. This thesis attempts to exploit these improvements into a method for including spatial information in SONAR processing and in general to image and signal analysis. Bayesian techniques for inclusion of prior knowledge and structuring complex problems are developed and applied to problems of texture segmentation, object detection and parameter extraction. It is shown through experiments on groundtruth and real datasets that the inclusion of spatial context can be very effective in improving poor techniques or, conversely in allowing simpler techniques to be used with the same objective outcome (with obvious computational advantages). The thesis also considers some of the implementation problems with the techniques used, and develops simple modifications to improve common algorithms.