Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International. Available also from UMI's Dissertation Abstracts.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Vita. Includes bibliographical references (leaves 119-133). Also available in electronic version.

This study was designed to explore the relationships between users and interactive images. Three factors were identified and provided different perspectives on how users interact with images: image utility, information-need, and images with varying levels of interactivity. The study used a mixed methodology to gain a more comprehensive understanding about the selected factors. An image survey was used to introduce the participants to the images and recorded utility values when given a specific task. The interviews allowed participants to provide details about their experiences with the interactive images and how it affected their utility values. Findings from the study showed that images offering the highest level of interactivity do not always generate the highest utility. Factors such as personal preference, specifically speed and control of the image, affect the usefulness of the image. Participant also provided a variety of uses where access to interactive images would be beneficial. Educational settings and research tools are a few examples of uses provided by participants.

A new approach, based on maximum likelihood, is developed for binary object image restoration. This considers the image formation process as a stochastic process, with noise as a random variable. The likelihood function is constructed for the cases of Gaussian and Poisson noise. An uphill climb method is used to find the object, defined by its "grain" positions, through maximizing the likelihood function for grain positions. In addition, some a priori information regarding object size and contour of shapes is used. This is summarized as a "neighbouring point" rule. Some examples of computer generated images with different signal-to-noise ratios are used to show the ability of the algorithm. These cases include both Gaussian and Poisson noise. For noiseless and low noise Gaussian cases, a modified uphill climb method is used. The results show that binary objects are fairly well restored for all noise cases considered.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

This paper first proposes a new quality metric for edge evaluation, based on six physical characteristics. These physical characteristics that affect human edge quality evaluation are continuity, smoothness, thinness, localization, detection and noisiness. The final edge quality score is a weighted linear combination of the quantified measures of these six edge quality attributes. The other feature of this edge evaluation metric is its adjustability. Through some training procedures, it can be adjusted to suit different user and application needs. In the latter part of this paper, an edge detector performance predictor is proposed. By a few initial measurements of image parameters, the performance of certain edge detectors can be predicted. Finally, the performance of several popular edge detectors is compared, under different variations of SNR, blurting and power spectrum.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Fong Chi Keung = 輪廓構圖法及其應用 / 馮志強. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Fong Chi Keung = Lun kuo gou tu fa ji qi ying yong / Feng Zhiqiang. / Acknowledgement --- p.i / Abstract --- p.ii / Contents --- p.iv / List of Figures --- p.vi / List of Tables --- p.xi / Chapter Chapter 1. --- Introduction --- p.1-1 / Chapter 1.1. --- A Brief Review on Image Representation --- p.1-1 / Chapter 1.2. --- Objective of the Research Work --- p.1-3 / Chapter 1.3. --- Organization of the Thesis --- p.1-4 / Chapter 1.3.1. --- The edge-model based representations --- p.1-4 / Chapter 1.3.2. --- The applications of edge-model based representation --- p.1-5 / Chapter Chapter 2. --- Review on the Edge Models --- p.2-1 / Chapter 2.1. --- Introduction --- p.2-1 / Chapter 2.2. --- Review on Existing Edge Models --- p.2-1 / Chapter 2.2.1. --- Unit-Step Model --- p.2-2 / Chapter 2.2.2. --- Ramp Model --- p.2-3 / Chapter 2.2.3. --- Hyperbolic Tangent Model --- p.2-4 / Chapter 2.2.4. --- van Beek's Edge Model --- p.2-5 / Chapter 2.3. --- Methodology --- p.2-6 / Chapter 2.3.1. --- Model Parameter Estimation in van Beek's model --- p.2-6 / Chapter 2.3.2. --- Model Parameter Estimation in other models --- p.2-9 / Chapter 2.3.3. --- Image Reconstruction --- p.2-10 / Chapter 2.3.4. --- Intensity Surface Reconstruction --- p.2-11 / Chapter 2.4. --- Summary --- p.2-18 / Chapter Chapter 3. --- Improved Edge-Model-Based representation --- p.3-1 / Chapter 3.1 --- Reconstruction Artifacts --- p.3-1 / Chapter 3.2 --- The improved edge model --- p.3-2 / Chapter 3.2.1. --- Minimum Reconstruction Range (MRR) --- p.3-2 / Chapter 3.2.2. --- Sub-pixel Estimation (SPE) --- p.3-4 / Chapter 3.3. --- Experimental Results --- p.3-11 / Chapter 3.3.1. --- Comparison between van Beek's Method and LSF in Parameters Estimation --- p.3-11 / Chapter 3.3.2. --- Comparison among Intensity Surface Reconstruction Methods --- p.3-13 / Chapter 3.3.3. --- Comparison among Edge Models --- p.3-18 / Chapter 3.4. --- Conclusions --- p.3-22 / Chapter Chapter 4. --- Edge-Model-Based Post-processing for SPIHT coded Images --- p.4-1 / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.2. --- Brief review on the Post-processing --- p.4-2 / Chapter 4.3. --- Experimental Results --- p.4-5 / Chapter 4.4. --- Conclusions --- p.4-6 / Chapter Chapter 5. --- Edge-Model-Based Interpolation --- p.5-1 / Chapter 5.1 --- Introduction --- p.5-1 / Chapter 5.2 --- Objectives --- p.5-6 / Chapter 5.3 --- Algorithm --- p.5-6 / Chapter 5.3.1. --- Edge Location Estimation --- p.5-7 / Chapter 5.3.2. --- Edge Width Correction --- p.5-10 / Chapter 5.3.3. --- Confident Function --- p.5-16 / Chapter 5.4 --- Experimental Results --- p.5-21 / Chapter 5.5 --- Conclusions --- p.5-32 / Chapter Chapter 6. --- Edge-model-based Image Segmentation --- p.6-1 / Chapter 6.1. --- Introduction --- p.6-1 / Chapter 6.2. --- A brief review on segmentation --- p.6-1 / Chapter 6.3. --- Objectives --- p.6-2 / Chapter 6.4. --- Theory --- p.6-3 / Chapter 6.5. --- Algorithm --- p.6-6 / Chapter 6.5.1. --- Pre-segmentation by edge-model --- p.6-6 / Chapter 6.5.2. --- Grouping by Gomory-Hu Tree --- p.6-8 / Chapter 6.6. --- Experimental Results --- p.6-10 / Chapter 6.7. --- Conclusions --- p.6-15 / Chapter Chapter 7. --- Conclusions and further developments --- p.7-1 / Chapter 7.1 --- Contributions and Conclusions --- p.7-1 / Chapter 7.1.1 --- Edge-Model-Based Post-Processing for SPIHT coded images --- p.7-1 / Chapter 7.1.2 --- Edge-Model-Based Interpolation --- p.7-2 / Chapter 7.1.3 --- Edge-Model-Based Segmentation --- p.7-2 / Chapter 7.2 --- Future Development --- p.7-3 / Appendix I. Test Images used in this Research --- p.I / Bibliography --- p.III

The Research Topic and the Need for It: This thesis starts with a review of what the literature says about the importance of corporate image and how it needs to be managed as a strategic asset. However, the problem is there is no model that shows what corporate image comprises and explains how its various components interact with one another so that it can be managed. The result is a number of confusing and contradictory definitions and unproductive discussions about things like whether corporate reputation and corporate image are different or synonymous. In response to this need, it is suggested that corporate image comprises three different image perspectives; namely, Self Image, Projected Image and Perceived Image and that it is only when these are defined separately and regarded holistically that corporate image can be properly defined, understood and managed. Objective: The objective of this research was to develop and test this model using triangulated approaches in which data could be acquired and understood from different sources. To this end questionnaires were developed by document analysis, consultation and discussion. This research was conducted in two very different organisations - a new university campus and a sugar co-operative. In the interests of confidentiality they have been renamed Barton University's Kingsley Campus and Sunstate Sugar Co-operative Association Limited. Data was input into both SPSS and HIPSYS computer programs for the Kingsley Campus research and into HIPSYS for the Sunstate Sugar research. For both research sites the results were discussed with members of all response groups so that accurate interpretations of the data could be made and additional meaningful data acquired. For Kingsley Campus, response groups included all Academic and General Staff of Kingsley Campus for the Self Image and the Projected Image, and for the Perceived Image all Current and Past Students, a representative sample of Grade 12 high school students, advanced diploma students of Kingsley TAFE, and Community Leaders. In all 3,693 questionnaires were distributed and 934 completed questionnaires were processed. For Sunstate Sugar, response groups included all employees for the Self Image separated by Management, Supervisors and Workforce, all Board members and all Employees who interface with the growers (members of the co-operative) for the Projected Image, and all members of the co-operative for the Perceived Image. In all 1830 questionnaires were distributed and 916 completed questionnaires were processed. Findings: The results from the Kingsley Campus research showed that the Projected Image needed to be more effectively targeted at the Grade 12 target group. The results from the Sunstate Sugar research showed that its Perceived Image was affected by its Self Image as well as by its Projected Image and that its Self Image in particular needed to be made more positive. The results from both organisations suggest that a positive corporate Self Image influences the Projected Image and can have as much impact on the Perceived Image as does the Projected Image. In exploring and discussing the results of this research, each organization derived recommendations which led to their developing action plans for the more effective management of their corporate image. These results indicate that the model created has eliminated a gap in the literature, diffused the confusion regarding what corporate image is, and provided a structure and a methodology by which corporate image can be identified and managed. It has been shown to have considerable utility.