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Detection and inspection of metal surface corrosion in the ballast tanks of U.S. Navy ships has been a long time problem. The adverse climatic conditions to which the ballast tanks are exposed and the uneven geometry of ballast tanks makes the visual inspection process of surface coatings a difficult job. Thousands of tanks are inspected yearly, with the average cost of an individual tank inspection at approximately $8-15 thousand/each. To aid the visual inspection process, this research is conducted to develop a new technique to automate the visual task of metal surface inspection by image acquisition and post processing. The best results of image processing are achieved by the enhanced contrast between the paint defect and the background using a newly developed optically active additive (OAA) used in paints. Thorough investigation of image processing algorithms has been carried out and a background of imaging theory and experiments is illustrated in this work.

This dissertation studies the dynamics behind sudden, negative shifts in the corporate reputations of business firms, through three independent but related papers, a phenomenon that we refer to as a reputational crisis. This issue is of critical importance because the corporate reputation of a firm is one of its most valuable but potentially volatile intangible resources. Therefore, a better understanding of the situations where business firms suffer significant reputational losses within relatively short periods of time can contribute to both strategic management and business and society. From a strategic management perspective, the examination of sudden major losses in corporate reputation is an examination of the loss of what is potentially one of the most important intangible firm resources, if not the most important intangible resource of the firm. While, from a business and society perspective, an examination of sudden drops in corporate reputation could reveal the reputational impact that such sudden events have in the network of stakeholders (Freeman, 1984) who surround the firm and are, in a sense, the 'evaluators' of its reputation. / The first paper of this dissertation consists of a theoretical exploration of the management of reputational crises caused by sudden and unexpected incidents like industrial accidents, scandals, and product failures. Drawing on the stakeholder and crisis management literatures, a model useful in providing a better understanding of reputational crises is developed. The second paper is an empirical investigation into the impact that accidents can have on the corporate reputation of business firms. More specifically the impact that a number of accident characteristics have on the reputational re-evaluations of two particular stakeholder groups, industry executives and financial analysts, is investigated with data drawn from Lexis-Nexis and the America's Most Admired Corporations (AMAC) survey of FORTUNE magazine. Finally, the third paper of the dissertation examines the Brent Spar controversy to investigate two issues of importance in the management of reputational crises: the reasons behind a company's decisions to buffer or bridge when faced with a reputational crisis; and, the role of stakeholder salience in this decision.

In this study, motion-compensated predictive coding of monochrome image sequences was investigated. The goals were to evaluate the performance limits of this coding method, to determine its relative advantages in comparison to other predictive coding techniques not compensated for motion, and to identify the factors which limit further improvements in performance. A motion-compensated prediction algorithm which estimates displacements only from previously-transmitted data was presented, and was shown to be nearly optimal. At low distortion levels, an adaptive intra-inter-frame predictor not compensated for motion was found to be almost as effective as motion-compensated prediction. At higher distortion levels, however, motion-compensated predictive coding becomes more attractive. The main factors which limit the performance of motion-compensated predictors were identified as: input noise, spatial interpolation errors, and the local nature of the displacement estimation methods. Methods to control these limiting factors were suggested.

In the present investigation, factors related to prospective applicants impressions of an organisation at the pre-interview stage of the recruitment process, and how these perceptions influence decisions to pursue an organisation for possible employment were explored. A heightened understanding of these factors is of relevance to organisations in the current labour market environment, and is of critical importance when considering that recruitment in the pre-interview stages remains under-researched and lacking in a strong theoretical foundation. To address this weakness in the recruitment research the present investigation will integrate two disparate areas of literature - recruitment and marketing - within the theoretical context of Fishbein and Ajzen's (1975) theory of reasoned action. The theory of reasoned action is well tested in the social psychology arena and provides a sound theoretical platform to underpin the relationships applicable to this investigation. In applying the marketing literature to the recruitment context, the concept of brand image is specifically utilised. Additionally, a problem that plagues much of the recruitment research is the heavy reliance on college and university students as a source of research data. The present study responds to this issue by sourcing data from a population of active job seekers submitting applications for advertised job vacancies at a large, Queensland-based higher education institution. A total of three hundred and fifty-one survey responses were obtained. The measures included perceptions of organisational image, attraction, and application intentions. The results indicated that there is support for the assertion that positive image perceptions held by applicants towards an employing organisation will lead to attraction to the organisation and active pursuit behaviour. Within this framework, it is evident that the 'impression management' capability of organisations in the contemporary business environment may hold the key to sustained competitive advantage in the critical search for qualified talent.

Efficient methods for the calculation of radial influence are described and applied to two image processing problems, digital halftoning and mixed content image compression. The methods operate recursively on scanlines of image values, spreading intensity from scanline to scanline in proportions approximating a Cauchy distribution. For error diffusion halftoning, experiments show that this recursive scanline spreading provides an ideal pattern of distribution of error. Error diffusion using masks generated to provide this distribution of error alleviate error diffusion "worm" artifacts. The recursive scanline by scanline application of a spreading filter and a complementary filter can be used to reconstruct an image from its horizontal and vertical pixel difference values. When combined with the use of a downsampled image the reconstruction is robust to incomplete and quantized pixel difference data. Such gradient field integration methods are described in detail proceeding from representation of images by gradient values along contours through to a variety of efficient algorithms. Comparisons show that this form of gradient field integration by convolution provides reduced distortion compared to other high speed gradient integration methods. The reduced distortion can be attributed to success in approximating a radial pattern of influence. An approach to edge-based image compression is proposed using integration of gradient data along edge contours and regularly sampled low resolution image data. This edge-based image compression model is similar to previous sketch based image coding methods but allows a simple and efficient calculation of an edge-based approximation image. A low complexity implementation of this approach to compression is described. The implementation extracts and represents gradient data along edge contours as pixel differences and calculates an approximate image by performing integration of pixel difference data by scanline convolution. The implementation was developed as a prototype for compression of mixed content image data in printing systems. Compression results are reported and strengths and weaknesses of the implementation are identified.

Efficient methods for the calculation of radial influence are described and applied to two image processing problems, digital halftoning and mixed content image compression. The methods operate recursively on scanlines of image values, spreading intensity from scanline to scanline in proportions approximating a Cauchy distribution. For error diffusion halftoning, experiments show that this recursive scanline spreading provides an ideal pattern of distribution of error. Error diffusion using masks generated to provide this distribution of error alleviate error diffusion "worm" artifacts. The recursive scanline by scanline application of a spreading filter and a complementary filter can be used to reconstruct an image from its horizontal and vertical pixel difference values. When combined with the use of a downsampled image the reconstruction is robust to incomplete and quantized pixel difference data. Such gradient field integration methods are described in detail proceeding from representation of images by gradient values along contours through to a variety of efficient algorithms. Comparisons show that this form of gradient field integration by convolution provides reduced distortion compared to other high speed gradient integration methods. The reduced distortion can be attributed to success in approximating a radial pattern of influence. An approach to edge-based image compression is proposed using integration of gradient data along edge contours and regularly sampled low resolution image data. This edge-based image compression model is similar to previous sketch based image coding methods but allows a simple and efficient calculation of an edge-based approximation image. A low complexity implementation of this approach to compression is described. The implementation extracts and represents gradient data along edge contours as pixel differences and calculates an approximate image by performing integration of pixel difference data by scanline convolution. The implementation was developed as a prototype for compression of mixed content image data in printing systems. Compression results are reported and strengths and weaknesses of the implementation are identified.

Efficient methods for the calculation of radial influence are described and applied to two image processing problems, digital halftoning and mixed content image compression. The methods operate recursively on scanlines of image values, spreading intensity from scanline to scanline in proportions approximating a Cauchy distribution. For error diffusion halftoning, experiments show that this recursive scanline spreading provides an ideal pattern of distribution of error. Error diffusion using masks generated to provide this distribution of error alleviate error diffusion "worm" artifacts. The recursive scanline by scanline application of a spreading filter and a complementary filter can be used to reconstruct an image from its horizontal and vertical pixel difference values. When combined with the use of a downsampled image the reconstruction is robust to incomplete and quantized pixel difference data. Such gradient field integration methods are described in detail proceeding from representation of images by gradient values along contours through to a variety of efficient algorithms. Comparisons show that this form of gradient field integration by convolution provides reduced distortion compared to other high speed gradient integration methods. The reduced distortion can be attributed to success in approximating a radial pattern of influence. An approach to edge-based image compression is proposed using integration of gradient data along edge contours and regularly sampled low resolution image data. This edge-based image compression model is similar to previous sketch based image coding methods but allows a simple and efficient calculation of an edge-based approximation image. A low complexity implementation of this approach to compression is described. The implementation extracts and represents gradient data along edge contours as pixel differences and calculates an approximate image by performing integration of pixel difference data by scanline convolution. The implementation was developed as a prototype for compression of mixed content image data in printing systems. Compression results are reported and strengths and weaknesses of the implementation are identified.

Thesis (Ph.D.)--University of Western Australia, 2004.

Thesis (M.S.)--University of Delaware, 2007. / Principal faculty advisor: Charles G. Boncelet, Dept. of Computer & Information Sciences. Includes bibliographical references.