Thesis (M.Phil.)--City University of Hong Kong, 2005. / "Submitted to Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 81-84)

Thesis (Ph.D.)--University of Bonn, Germany. / Includes bibliographical references and index. Also available in print.

Thesis (Ph. D.)--Lehigh University, 2005. / Includes vita. Includes bibliographical references (leaves 163-177).

Thesis (Ph.D.)--University of Bonn, Germany. / Includes bibliographical references and index.

Thesis (MScIng)--University of Stellenbosch, 2005. / Bibliography. Also available via the Internet.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xi, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 90-95).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 72-74). Also available in electronic version.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 84-92).

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

The ability to precisely determine one's location in within the world (localisation) is a key requirement for any robot wishing to navigate through the world. For long-term operation, such a localisation system must be robust to changes in the environment, both short term (eg. traffic, weather) and long term (eg. seasons). This thesis presents two methods for performing such localisation using cameras - small, cheap, lightweight sensors that are universally available. Whilst many image-based localisation systems have been proposed in the past, they generally rely on either feature matching, which fails under many degradations such as motion blur, or on photometric consistency, which fails under changing illumination. The methods we propose here directly align images with a dense prior map. The first method uses maps synthesised from a combination of LIDAR scanners to generate geometry and cameras to generate appearance, whilst the second uses vision for both mapping and localisation. Both make use of an information-theoretic metric, Normalised Information Distance (NID), for image alignment, relaxing the appearance constancy assumption inherent in photometric methods. Our methods require significant computational resources, but through the use of commodity GPUs, we are able to run them at a rate of 8-10Hz. Our GPU implementations make use of low level OpenGL, enabling compatibility across almost any GPU hardware. We also present a method for calibrating multi-sensor systems, enabling the joint use of cameras and LIDAR for mapping. Through experiments on both synthetic data and real-world data from over 100km of driving outdoors, we demonstrate the robustness of our localisation system to large variations in appearance. Comparisons with state-of-the-art feature-based and direct methods show that ours is significantly more robust, whilst maintaining similar precision.