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Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.

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Using a co-ethnographic approach to focus on one person's story, we explore how a sense of place may be evident in self constructed Gypsy-Traveller identity and narrative. Mary's recounting of her experiences of living and growing up in the Caldewgate district of Carlisle (UK) illustrates the place of family relations as a key element of Gypsy-Traveller self identity and suggests, we believe, the centrality of family and internal relationships as a strong feature in the construction of personal notions and narratives of place for Gypsy-Traveller people.

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In the domain of computer vision, entropy—defined as a measure of irregularity—has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well.

We would like to thank all of the people that have offered their assistance in the development of this Capstone Project. Specifically, we would like to acknowledge Eira Klich-Heartt for her advising and support, and Ruth Ramsey for her supportive guidance, suggestions, and editing in her role of our second reader. We are also very appreciative for each of the participants in this study who allowed us to interview them and conduct this research project based upon their experiences and insights. We would like to acknowledge our families and significant others for their never-ending patience and support throughout this process. We would also like to acknowledge our friends who have stayed by our side, even though we often had to delay or break plans because the work took over our lives. We would also like to acknowledge our coworkers and employers for their flexibility in our schedules so that we could attend meetings and take time to complete this project. In addition to the many people who mean so much to us that we have dedicated not only our efforts in this master's thesis to, but also the efforts of completing the entire requirements of this MSOT program at Dominican, we thank Grace for granting us fortitude, perseverance, humility, and teamwork throughout it all. Without which, we would have lost sight of the gravity of our endeavors and achievements, as well as the significance and potential this manuscript has for future discussion and research to come.