This thesis develops a self-adaptive architecture for image understanding that addresses certain kinds of lack of robustness common in image understanding programs. The architecture provides support for making image understanding programs that can manipulate their own semantics and thereby adjust their structure in response to changes in the environment that might cause static image understanding systems to fail. The general approach taken has been to explore the ideas of self-adaptive software and implement an architectural framework that addresses a class of problems that we term "interpretation problems" common in image understanding. Self-adaptive software is a relatively new idea and this thesis represents one of the first implementations of the general idea. The general idea is that to make programs robust to changing environmen- tal conditions that they should be "aware" of their relationship with the environment and be able to restructure themselves at runtime in order to "track" changes in the environment. The implementation takes the form of a multi-layered reflective interpreter that ma- nipulates and runs simple agents. The interpreter framework utilizes Monte-Carlo sam- pling as a mechanism for estimating most likely solutions, uses Minimum Descriptin Length (MDL) as a central coordinating device, and includes a theorem prover based compiler to restructure the program when necessary. To test the architectural ideas developed in the thesis a test domain of interpreting aerial images was chosen. Much of the research described in the thesis addresses issues in that problem domain. The task of the program is to segment, label, and parse aerial images so as to produce an image description similar to descriptions produced by a human expert. An image corpus is developed that is used as the source of domain knowledge. The first processing stage of the program segments the aerial images into segments similar to those found in the annotated corpus. To accomplish this a new segmentation algorithm that we call semantic segmentation was developed that not only used MDL as a principle to drive the low-level segmentation but also allows higher level semantics to influence the segmentation. In our usage of the algorithm those semantics take the form of labeling and parsing the resulting segmentation. The second stage labels the regions and parses the regions into a parse tree. To do this we develop a 2D statistical parser. Rules of grammar are induced from the corpus and an MDL parser finds approximations to the most probable parse of the regions of the segmented image.

Thesis (Ph.D.)--University of Adelaide, 2004. / Photocopy. Includes bibliographical references (leaves 283-293).

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [130]-138).

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Anuj Srivastava, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Jan. 12, 2005). Includes bibliographical references.

Thesis (Ph. D.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains x, 132 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 124-132).

Thesis (MS )--Montana State University--Bozeman, 2008. / Typescript. Chairperson, Graduate Committee: Rick L. Lawrence. Includes bibliographical references (leaves 71-77).

Thesis (Ph. D.)--University of Missouri-Columbia, 2004. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (June 29, 2005) Vita. Includes bibliographical references.

Local participation in environmental decision making processes is a recognized need if the goals of sustainable development are to be met. Spatial information is an important part of environmental decision making, but so far, technical barriers have prevented effective public participation in spatial data management and analysis. These barriers need to be overcome if participants are to take part in a decision making process in a manner that is both fair and competent. The study was undertaken to quantify land cover change in a particular region and, through this exercise, to determine what the practical barriers to public participation in decision making might be. The work was conducted in the Windermere Valley, British Columbia. Community questions about local environmental change were determined from a local newspaper and discussions with Environmental Non-Governmental Organizations (ENGO's). Using satellite imagery and other geospatial data, community questions about local environmental change were answered through the detection of land cover change for the period 1974--1991. The processes of acquiring the data and completing the analysis were evaluated with the criteria of fairness and competence. The products of the change detection analysis were evaluated based on how well they answered community questions. Suggestions are presented on what tools and resources ENGO's would require to complete a similar study to answer questions about the environment.

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