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No / The role of radiographers with respect to image interpretation within clinical practice is well recognised. It is the expectation of the professional, regulatory and academic bodies that upon qualification, radiographers will possess image interpretation skills. Additionally, The College of Radiographers has asserted that its aspiration is for all radiographers to be able to provide an immediate written interpretation on skeletal trauma radiographs by 2010. This paper explores the readiness of radiography education programmes in the UK to deliver this expectation. Method A postal questionnaire was distributed to 25 Higher Education Institutions in the UK (including Northern Ireland) that provided pre-registration radiography education as identified from the Society & College of Radiographers register. Information was sought relating to the type of image interpretation education delivered at pre- and post-registration levels; the anatomical range of image interpretation education; and education delivery styles. Results A total of 19 responses (n=19/25; 76.0%) were received. Image interpretation education was included as part of all radiographer pre-registration programmes and offered at post-registration level at 12 academic centres (n=12/19; 63.2%). The anatomical areas and educational delivery methods varied across institutions. Conclusion Radiography education providers have embraced the need for image interpretation education within both pre- and post-registration radiography programmes. As a result, UK education programmes are able to meet the 2010 College of Radiographers aspiration.

Since the 1970s when the first destination image studies were performed, this topic has become one of the most predominant in the tourism marketing literature. Destination image within the tourism industry is essential, as most tourism products are services rather than physical goods, and can often only compete by means of the image they portray. The image of a specific destination is a major element in the final decision when selecting the destination. Both positive and negative images occur, together having a great impact on the travel and tourism industry. Destinations therefore have to create images of their location and what they have to offer to help differentiate them from their competition. Therefore, potential tourists rely on their mental images when deciding to visit one destination over another. Different influences emerge within tourist decisions, which affect their ultimate experience. It is therefore clear that, to understand tourists‟ needs and wants, relationship building is important and this could assist with the marketing of products or services. Marketing plays a central part in tourism, since consumers need to travel to a certain destination to see, feel or test the product that is to be purchased and evaluated. Image is formed based on three main components. These are: cognitive (what one knows about a destination), affective (how one feels about what one knows) and conative components (how one acts on this information). To date, various image models have been developed. However, none of these have been applied to, tested in, or developed for South Africa. It is therefore important to know how tourists formulate a destinations‟ image as well as what influences their image regarding a destination. Therefore, to achieve this and the goal of this study, which is to develop a framework for the optimisation of the image of South Africa as a tourism destination, a comprehensive review of marketing and destination image literature was performed, subsequent to which the research was conducted. After having conducted the literature review and gathered expert advice and opinions, various literature-based attributes were identified. A total of sixty-three attributes were acknowledged whereafter these were sifted and grouped into Cognitive, Affective and Conative attributes. After taking expert advice into consideration, these attributes were once again sifted and it was determined whether they were applicable for this research. A total of fifty-seven attributes remained important and formed part of the questionnaire. Forty-two attributes were Cognitive, twelve Affective and three Conative. The research was conducted at the international departure area of a major international airport in South Africa. The respondents consisted of international tourists that were returning to their home countries after visiting South Africa. A total of 500 questionnaires were distributed of which 474 questionnaires were obtained. Of these, 451 questionnaires were usable for this study, as 23 questionnaires were incomplete and not usable. The number of questionnaires was therefore representative of the target population and further analysis. After the questionnaires for this study were gathered, the primary data was captured and analysed. Different types of data analyses were used in this study: Firstly, descriptive analysis to determine findings concerning the demographic profile of respondents and the respondent‟s travel behaviour whilst visiting South Africa. Secondly, factor analyses to factorise the image attributes into image factors; and to factorise external aspects into factors and determine how these affect image formation. Thirdly, ANOVAs (One-way analysis of variance) were conducted where more than two categories formed part of the question, t-tests were conducted to compare the image factors with questions consisting of only two categories and Spearman rank correlations were conducted to describe the strength and direction of the linear relationship between selected variables. Finally, Structural Equation Modelling was used to empirically test the framework and evaluate how well the data supports the hypothesised model. The first factor analysis resulted in 13 reliable and valid factors, which consisted of the cognitive, affective and conative image attributes. These factors, together with the factors of the second factor analysis (Media, Political and Iconic aspects) were used as constructs in the Structural Equation Modelling analysis. After having combined the results of all the different analyses, a framework was developed that identifies the aspects influencing South Africa‟s image. Some of the main findings were that media, political happenings and iconic aspects directly influenced cognitive, affective and conative images. Novel to this study was the significant influence of icons. Interestingly, demographic information only affects cognitive image and neither affective nor conative image. Travel behaviour contributes to the formation of cognitive, affective and conative image.However, surprisingly, the lack of influence from travel agents and travel guides was also depicted in the results. This framework emphasises the importance of pre-, onsite and post-experiences as well as communication in image formation. This study contributes academically, methodologically and practically. Academic contributions include empirically testing the framework, which significantly contributes to literature; and the innovative inclusion and assessment of icons adds a new dimension to image formation in literature. From a methodological point of view, it is clear that the analyses of all influencing aspects are challenging and not standardised. The types of analyses applied in this study enhanced the in-depth analyses of the data that was then included into one framework. The data was empirically tested and found to be reliable. The empirical testing of all aspects in a South African context was different and innovative, which finally created a detailed picture of South Africa‟s image as a tourism destination. Finally, the practical contribution of this study is that the framework developed for this study can be used by tourism organisations of various types in planning and implementing marketing strategies. The framework can direct their advertising and staff training; and improve the general tourism product of South Africa. The framework can also be applied to other tourism destinations. Clear recommendations were made regarding the focus of marketing strategies and building the image of South Africa. It was recommended that the framework developed in this study be implemented by national tourism organisations such as SA Tourism, as well as provincial organisations such as Tourism Boards. Product owners can benefit from the framework by considering some of the influential aspects in their product development and marketing strategies. Lastly, all marketing strategies and plans for South Africa should be focused on improving the cognitive, affective and conative image of South Africa. / PhD (Tourism Management), North-West University, Potchefstroom Campus, 2015

Since the 1970s when the first destination image studies were performed, this topic has become one of the most predominant in the tourism marketing literature. Destination image within the tourism industry is essential, as most tourism products are services rather than physical goods, and can often only compete by means of the image they portray. The image of a specific destination is a major element in the final decision when selecting the destination. Both positive and negative images occur, together having a great impact on the travel and tourism industry. Destinations therefore have to create images of their location and what they have to offer to help differentiate them from their competition. Therefore, potential tourists rely on their mental images when deciding to visit one destination over another. Different influences emerge within tourist decisions, which affect their ultimate experience. It is therefore clear that, to understand tourists‟ needs and wants, relationship building is important and this could assist with the marketing of products or services. Marketing plays a central part in tourism, since consumers need to travel to a certain destination to see, feel or test the product that is to be purchased and evaluated. Image is formed based on three main components. These are: cognitive (what one knows about a destination), affective (how one feels about what one knows) and conative components (how one acts on this information). To date, various image models have been developed. However, none of these have been applied to, tested in, or developed for South Africa. It is therefore important to know how tourists formulate a destinations‟ image as well as what influences their image regarding a destination. Therefore, to achieve this and the goal of this study, which is to develop a framework for the optimisation of the image of South Africa as a tourism destination, a comprehensive review of marketing and destination image literature was performed, subsequent to which the research was conducted. After having conducted the literature review and gathered expert advice and opinions, various literature-based attributes were identified. A total of sixty-three attributes were acknowledged whereafter these were sifted and grouped into Cognitive, Affective and Conative attributes. After taking expert advice into consideration, these attributes were once again sifted and it was determined whether they were applicable for this research. A total of fifty-seven attributes remained important and formed part of the questionnaire. Forty-two attributes were Cognitive, twelve Affective and three Conative. The research was conducted at the international departure area of a major international airport in South Africa. The respondents consisted of international tourists that were returning to their home countries after visiting South Africa. A total of 500 questionnaires were distributed of which 474 questionnaires were obtained. Of these, 451 questionnaires were usable for this study, as 23 questionnaires were incomplete and not usable. The number of questionnaires was therefore representative of the target population and further analysis. After the questionnaires for this study were gathered, the primary data was captured and analysed. Different types of data analyses were used in this study: Firstly, descriptive analysis to determine findings concerning the demographic profile of respondents and the respondent‟s travel behaviour whilst visiting South Africa. Secondly, factor analyses to factorise the image attributes into image factors; and to factorise external aspects into factors and determine how these affect image formation. Thirdly, ANOVAs (One-way analysis of variance) were conducted where more than two categories formed part of the question, t-tests were conducted to compare the image factors with questions consisting of only two categories and Spearman rank correlations were conducted to describe the strength and direction of the linear relationship between selected variables. Finally, Structural Equation Modelling was used to empirically test the framework and evaluate how well the data supports the hypothesised model. The first factor analysis resulted in 13 reliable and valid factors, which consisted of the cognitive, affective and conative image attributes. These factors, together with the factors of the second factor analysis (Media, Political and Iconic aspects) were used as constructs in the Structural Equation Modelling analysis. After having combined the results of all the different analyses, a framework was developed that identifies the aspects influencing South Africa‟s image. Some of the main findings were that media, political happenings and iconic aspects directly influenced cognitive, affective and conative images. Novel to this study was the significant influence of icons. Interestingly, demographic information only affects cognitive image and neither affective nor conative image. Travel behaviour contributes to the formation of cognitive, affective and conative image.However, surprisingly, the lack of influence from travel agents and travel guides was also depicted in the results. This framework emphasises the importance of pre-, onsite and post-experiences as well as communication in image formation. This study contributes academically, methodologically and practically. Academic contributions include empirically testing the framework, which significantly contributes to literature; and the innovative inclusion and assessment of icons adds a new dimension to image formation in literature. From a methodological point of view, it is clear that the analyses of all influencing aspects are challenging and not standardised. The types of analyses applied in this study enhanced the in-depth analyses of the data that was then included into one framework. The data was empirically tested and found to be reliable. The empirical testing of all aspects in a South African context was different and innovative, which finally created a detailed picture of South Africa‟s image as a tourism destination. Finally, the practical contribution of this study is that the framework developed for this study can be used by tourism organisations of various types in planning and implementing marketing strategies. The framework can direct their advertising and staff training; and improve the general tourism product of South Africa. The framework can also be applied to other tourism destinations. Clear recommendations were made regarding the focus of marketing strategies and building the image of South Africa. It was recommended that the framework developed in this study be implemented by national tourism organisations such as SA Tourism, as well as provincial organisations such as Tourism Boards. Product owners can benefit from the framework by considering some of the influential aspects in their product development and marketing strategies. Lastly, all marketing strategies and plans for South Africa should be focused on improving the cognitive, affective and conative image of South Africa. / PhD (Tourism Management), North-West University, Potchefstroom Campus, 2015

La détection de changements dans une scène est l’un des problèmes les plus complexes en télédétection. Il s’agit de détecter des modifications survenues dans une zone géographique donnée par comparaison d’images de cette zone acquises à différents instants. La comparaison est facilitée lorsque les images sont issues du même type de capteur c’est-à-dire correspondent à la même modalité (le plus souvent optique multi-bandes) et possèdent des résolutions spatiales et spectrales identiques. Les techniques de détection de changements non supervisées sont, pour la plupart, conçues spécifiquement pour ce scénario. Il est, dans ce cas, possible de comparer directement les images en calculant la différence de pixels homologues, c’est-à-dire correspondant au même emplacement au sol. Cependant, dans certains cas spécifiques tels que les situations d’urgence, les missions ponctuelles, la défense et la sécurité, il peut s’avérer nécessaire d’exploiter des images de modalités et de résolutions différentes. Cette hétérogénéité dans les images traitées introduit des problèmes supplémentaires pour la mise en œuvre de la détection de changements. Ces problèmes ne sont pas traités par la plupart des méthodes de l’état de l’art. Lorsque la modalité est identique mais les résolutions différentes, il est possible de se ramener au scénario favorable en appliquant des prétraitements tels que des opérations de rééchantillonnage destinées à atteindre les mêmes résolutions spatiales et spectrales. Néanmoins, ces prétraitements peuvent conduire à une perte d’informations pertinentes pour la détection de changements. En particulier, ils sont appliqués indépendamment sur les deux images et donc ne tiennent pas compte des relations fortes existant entre les deux images. L’objectif de cette thèse est de développer des méthodes de détection de changements qui exploitent au mieux l’information contenue dans une paire d’images observées, sans condition sur leur modalité et leurs résolutions spatiale et spectrale. Les restrictions classiquement imposées dans l’état de l’art sont levées grâce à une approche utilisant la fusion des deux images observées. La première stratégie proposée s’applique au cas d’images de modalités identiques mais de résolutions différentes. Elle se décompose en trois étapes. La première étape consiste à fusionner les deux images observées ce qui conduit à une image de la scène à haute résolution portant l’information des changements éventuels. La deuxième étape réalise la prédiction de deux images non observées possédant des résolutions identiques à celles des images observées par dégradation spatiale et spectrale de l’image fusionnée. Enfin, la troisième étape consiste en une détection de changements classique entre images observées et prédites de mêmes résolutions. Une deuxième stratégie modélise les images observées comme des versions dégradées de deux images non observées caractérisées par des résolutions spectrales et spatiales identiques et élevées. Elle met en œuvre une étape de fusion robuste qui exploite un a priori de parcimonie des changements observés. Enfin, le principe de la fusion est étendu à des images de modalités différentes. Dans ce cas où les pixels ne sont pas directement comparables, car correspondant à des grandeurs physiques différentes, la comparaison est réalisée dans un domaine transformé. Les deux images sont représentées par des combinaisons linéaires parcimonieuses des éléments de deux dictionnaires couplés, appris à partir des données. La détection de changements est réalisée à partir de l’estimation d’un code couplé sous condition de parcimonie spatiale de la différence des codes estimés pour chaque image. L’expérimentation de ces différentes méthodes, conduite sur des changements simulés de manière réaliste ou sur des changements réels, démontre les avantages des méthodes développées et plus généralement de l’apport de la fusion pour la détection de changements / Change detection is one of the most challenging issues when analyzing remotely sensed images. It consists in detecting alterations occurred in a given scene from between images acquired at different times. Archetypal scenarios for change detection generally compare two images acquired through the same kind of sensor that means with the same modality and the same spatial/spectral resolutions. In general, unsupervised change detection techniques are constrained to two multiband optical images with the same spatial and spectral resolution. This scenario is suitable for a straight comparison of homologous pixels such as pixel-wise differencing. However, in somespecific cases such as emergency situations, punctual missions, defense and security, the only available images may be of different modalities and of different resolutions. These dissimilarities introduce additional issues in the context of operational change detection that are not addressedby most classical methods. In the case of same modality but different resolutions, state-of-the artmethods come down to conventional change detection methods after preprocessing steps appliedindependently on the two images, e.g. resampling operations intended to reach the same spatialand spectral resolutions. Nevertheless, these preprocessing steps may waste relevant informationsince they do not take into account the strong interplay existing between the two images. The purpose of this thesis is to study how to more effectively use the available information to work with any pair of observed images, in terms of modality and resolution, developing practicalcontributions in a change detection context. The main hypothesis for developing change detectionmethods, overcoming the weakness of classical methods, is through the fusion of observed images. In this work we demonstrated that if one knows how to properly fuse two images, it is also known how to detect changes between them. This strategy is initially addressed through a change detection framework based on a 3-step procedure: fusion, prediction and detection. Then, the change detection task, benefiting from a joint forward model of two observed images as degradedversions of two (unobserved) latent images characterized by the same high spatial and highspectral resolutions, is envisioned through a robust fusion task which enforces the differencesbetween the estimated latent images to be spatially sparse. Finally, the fusion problem isextrapolated to multimodal images. As the fusion product may not be a real quantity, the process is carried out by modelling both images as sparse linear combinations of an overcomplete pair of estimated coupled dictionaries. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes corresponding to each image. Experiments conducted in simulated realistically and real changes illustrate the advantages of the developed method, both qualitatively and quantitatively, proving that the fusion hypothesis is indeed a real and effective way to deal with change detection

Seismic layer segmentation, oil-gas boundary surfaces recognition, and 3D volume data reconstruction are three important tasks in three-dimensional seismic image processing. Geophysical and geological parameters and properties have been known to exhibit progressive changes in a layer. However, there are also times when sudden changes can occur between two layers. λ-connectedness was proposed to describe such a phenomenon. Based on graph theory, λ-connectedness describes the relationship among pixels in an image. It is proved that λ-connectedness is an equivalence relation. That is, it can be used to partition an image into different classes and hence can be used to perform image segmentation. Using the random graph theory and λ-connectivity of the image, the length of the path in a λ-connected set can be estimated. In addition to this, the normal λ-connected subsets preserve every path that is λ-connected in the subsets. An O(nlogn) time algorithm is designed for the normal λ-connected segmentation. Techniques developed are used to find objects in 2D/3D seismic images. Finding the interface between two layers or finding the boundary surfaces of an oil-gas reserve is often asked. This is equivalent to finding out whether a λ-connected set is an interface or surface. The problem that is raised is how to recognize a surface in digital spaces. λ-connectedness is a natural and intuitive way for describing digital surfaces and digital manifolds. Fast algorithms are designed to recognize whether an arbitrary set is a digital surface. Furthermore, the classification theorem of simple surface points is deduced: there are only six classes of simple surface points in 3D digital spaces. Our definition has been proved to be equivalent to Morgenthaler-Rosenfeld's definition of digital surfaces in direct adjacency. Reconstruction of a surface and data volume is important to the seismic data processing. Given a set of guiding pixels, the problem of generating a λ-connected (subset of image) surface is an inverted problem of λ-connected segmentation. In order to simplify the fitting algorithm, gradual variation, an equivalent concept of λ-connectedness, is used to preserve the continuity of the fitted surface. The key theorem, the necessary and sufficient condition for the gradually varied interpolation, has been mathematically proven. A random gradually varied surface fitting is designed, and other theoretical aspects are investigated. The concepts are used to successfully reconstruct 3D seismic real data volumes. This thesis proposes λ-connectedness and its applications as applied to seismic data processing. It is used for other problems such as ionogram scaling and object tracking. It has the potential to become a general technique in image processing and computer vision applications. Concepts and knowledge from several areas in mathematics such as Set Theory, Fuzzy Set Theory, Graph Theory, Numerical Analysis, Topology, Discrete Geometry, Computational Complexity, and Algorithm Design and Analysis have been applied to the work of this thesis.

We present a new technique for creating an editable vector graphic from an object in a raster image. Object selection is performed interactively in subsecond time by calling graph cut with each mouse movement. A renderable mesh is then computed automatically for the selected object and each of its (sub)objects by (1) generating a coarse object mesh; (2) performing recursive graph cut segmentation and hierarchical ordering of subobjects; (3) applying error-driven mesh refinement to each (sub)object. The result is a fully layered object hierarchy that facilitates object-level editing without leaving holes. Object-based vectorization compares favorably with current approaches in the representation and rendering quality. Object-based vectorization and complex editing tasks are performed in a few 10s of seconds.

In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image generation can be further divided into two subtasks, i.e., cross-view image translation and semantic image synthesis. For each task, we have proposed the corresponding solution. Specifically, for hand gesture generation, we have proposed the GestureGAN framework. For facial expression generation, we have proposed the Cycle-in-Cycle GAN (C2GAN) framework. For person pose generation, we have proposed the XingGAN and BiGraphGAN frameworks. For cross-view image translation, we have proposed the SelectionGAN framework. For semantic image synthesis, we have proposed the Local and Global GAN (LGGAN), EdgeGAN, and Dual Attention GAN (DAGAN) frameworks. Although each method was originally proposed for a certain task, we later discovered that each method is universal and can be used to solve different tasks. For instance, GestureGAN can be used to solve both hand gesture generation and cross-view image translation tasks. C2GAN can be used to solve facial expression generation, person pose generation, hand gesture generation, and cross-view image translation. SelectionGAN can be used to solve cross-view image translation, facial expression generation, person pose generation, hand gesture generation, and semantic image synthesis. Moreover, we explore cross-modal translation and propose a novel DanceGAN for audio-to-video translation.

Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of-the-art UNIT methods are either built from several two-domain mappings that are required to be learned independently or they generate low-diversity results, a phenomenon also known as model collapse. (2) Most of the manipulation is with the assistance of visual maps or digital labels without exploring natural languages, which could be more scalable and flexible in practice. (3) In an MMUIT system, the style latent space is usually disentangled between every two image domains. While interpolations within domains are smooth, interpolations between two different domains often result in unrealistic images with artifacts when interpolating between two randomly sampled style representations from two different domains. Improving the smoothness of the style latent space can lead to gradual interpolations between any two style latent representations even between any two domains. (4) It is expensive to train MMUIT models from scratch at high resolution. Interpreting the latent space of pre-trained unconditional GANs can achieve pretty good image translations, especially high-quality synthesized images (e.g., 1024x1024 resolution). However, few works explore building an MMUIT system with such pre-trained GANs. In this thesis, we focus on these vital issues and propose several techniques for building better MMUIT systems. First, we base on the content-style disentangled framework and propose to fit the style latent space with Gaussian Mixture Models (GMMs). It allows a well-trained network using a shared disentangled style latent space to model multi-domain translations. Meanwhile, we can randomly sample different style representations from a Gaussian component or use a reference image for style transfer. Second, we show how the GMM-modeled latent style space can be combined with a language model (e.g., a simple LSTM network) to manipulate multiple styles by using textual commands. Then, we not only propose easy-to-use constraints to improve the smoothness of the style latent space in MMUIT models, but also design a novel metric to quantitatively evaluate the smoothness of the style latent space. Finally, we build a new model to use pretrained unconditional GANs to do MMUIT tasks.

This research introduces an image retrieval system which is, in different ways, inspired by the human vision system. The main problems with existing machine vision systems and image understanding are studied and identified, in order to design a system that relies on human image understanding. The main improvement of the developed system is that it uses the human attention principles in the process of image contents identification. Human attention shall be represented by saliency extraction algorithms, which extract the salient regions or in other words, the regions of interest. This work presents a new approach for the saliency identification which relies on the irregularity of the region. Irregularity is clearly defined and measuring tools developed. These measures are derived from the formality and variation of the region with respect to the surrounding regions. Both local and global saliency have been studied and appropriate algorithms were developed based on the local and global irregularity defined in this work. The need for suitable automatic clustering techniques motivate us to study the available clustering techniques and to development of a technique that is suitable for salient points clustering. Based on the fact that humans usually look at the surrounding region of the gaze point, an agglomerative clustering technique is developed utilising the principles of blobs extraction and intersection. Automatic thresholding was needed in different stages of the system development. Therefore, a Fuzzy thresholding technique was developed. Evaluation methods of saliency region extraction have been studied and analysed; subsequently we have developed evaluation techniques based on the extracted regions (or points) and compared them with the ground truth data. The proposed algorithms were tested against standard datasets and compared with the existing state-of-the-art algorithms. Both quantitative and qualitative benchmarking are presented in this thesis and a detailed discussion for the results has been included. The benchmarking showed promising results in different algorithms. The developed algorithms have been utilised in designing an integrated saliency-based image retrieval system which uses the salient regions to give a description for the scene. The system auto-labels the objects in the image by identifying the salient objects and gives labels based on the knowledge database contents. In addition, the system identifies the unimportant part of the image (background) to give a full description for the scene.