Many smooth, highly specular coatings such as automotive paints are subjected to considerable performance demands as the customer expectations for appearance of coatings are continually increasing. Therefore it is vital to develop robust methods to monitor surface quality online. An automated visual assessment of specular coated surface that would not only provide a cost effective and reliable solution to the industries but also facilitate the implementation of a real-time feedback loop. The scope of this thesis is a subset of the inspection technology that facilitates real-time close loop control of the surface quality and concentrates on one common surface defect the seed defect. This machine vision system design utilizes surface reflectance models as a rational basis. Using a single high-contrast image the height of the seed defect is computed; the result is obtained rapidly and is reasonably accurate approximation of the actual height.

In an era where Instagram is the new dominating social media platform to reach and communicate with consumers, the demands on companies to differentiate their social media content have increased. Executives seem to avoid social media due to absence of how to manage and learn from it. Therefore, additional in-depth knowledge of how to place apparel products in favourable contexts through images could make marketing efforts more efficient on Instagram. The path this dissertation follows is qualitative with an abductive approach, since the aim of this dissertation is to create an in-depth understanding of what image attributes influence and motivate consumers on Instagram. Primary data have been produced through three semi-structured focus group interviews along with secondary data collected from five apparel brands’ Instagram accounts. This thesis takes its ground in consumer behaviour and theories of visual content, but does not rely on theory alone, as it would have prevented findings of new insights. Findings of previous research on how visual content influence consumers reinforced our research study even when displayed in the forum of Instagram. Additionally, the results of this dissertation stress the importance of placing the product in a relevant context, to a visually appealing background and preferably shown on a human being. These findings may be useful as guidance for apparel companies using Instagram as a promotional tool, as well as for companies who are planning to do so.

Magister Commercii - MCom / This study aimed to shed insight on how store image influences customer perception. Everything customers see, hear and experience is linked together and forms their overall perception of a store. The first objective of the study was to analyze whether a relationship exists between the store choice and customers biographical details. The second objective is to identify components of store image that shoppers may consider important in store selection process; how the case company can use this knowledge and develop the business and customer service even further. Eight dimensions of shopping enjoyment are proposed and a 47-item measure was developed to measure 155 consumer perceptions from various malls in the geographical area of Cape Town. Findings indicate that there are no statistically significant relations between store image and consumers demographic factors such as age, gender, level of education, marital status, occupation and income. Furthermore, respondents rated physical characteristics of the store which included factors such as the neatness and cleanliness of the store, its decor, the wideness of the aisles, air-conditioning and lighting as the most important element when making a store choice. The implications of these results are discussed, together with practical and theoretical implications, study limitations, and future research directions.

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

Alzheimer’s disease (AD) is a chronic and progressive, irreversible syndrome that deteriorates the cognitive functions. Official death certificates of 2013 reported 84,767 deaths from Alzheimer’s disease, making it the 6th leading cause of death in the United States. The rate of AD is estimated to double by 2050. The neurodegeneration of AD occurs decades before symptoms of dementia are evident. Therefore, having an efficient methodology for the early and proper diagnosis can lead to more effective treatments. Neuroimaging techniques such as magnetic resonance imaging (MRI) can detect changes in the brain of living subjects. Moreover, medical imaging techniques are the best diagnostic tools to determine brain atrophies; however, a significant limitation is the level of training, methodology, and experience of the diagnostician. Thus, Computer aided diagnosis (CAD) systems are part of a promising tool to help improve the diagnostic outcomes. No publications addressing the use of Feedforward Artificial Neural Networks (ANN), and MRI image attributes for the classification of AD were found. Consequently, the focus of this study is to investigate if the use of MRI images, specifically texture and frequency attributes along with a feedforward ANN model, can lead to the classification of individuals with AD. Moreover, this study compared the use of a single view versus a multi-view of MRI images and their performance. The frequency, texture, and MRI views in combination with the feedforward artificial neural network were tested to determine if they were comparable to the clinician’s performance. The clinician’s performances used were 78 percent accuracy, 87 percent sensitivity, 71 percent specificity, and 78 percent precision from a study with 1,073 individuals. The study found that the use of the Discrete Wavelet Transform (DWT) and Fourier Transform (FT) low frequency give comparable results to the clinicians; however, the FT outperformed the clinicians with an accuracy of 85 percent, precision of 87 percent, sensitivity of 90 percent and specificity of 75 percent. In the case of texture, a single texture feature, and the combination of two or more features gave results comparable to the clinicians. However, the Gray level co-occurrence matrix (GLCOM), which is the combination of texture features, was the highest performing texture method with 82 percent accuracy, 86 percent sensitivity, 76 percent specificity, and 86 percent precision. Combination CII (energy and entropy) outperformed all other combinations with 78 percent accuracy, 88 percent sensitivity, 72 percent specificity, and 78 percent precision. Additionally, a combination of views can increase performance for certain texture attributes; however, the axial view outperformed the sagittal and coronal views in the case of frequency attributes. In conclusion, this study found that both texture and frequency characteristics in combinations with a feedforward backpropagation neural network can perform at the level of the clinician and even higher depending on the attribute and the view or combination of views used.