Scholarship & Research
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Item Structures of cultural memory: the photography of Tom Wright(Montana State University - Bozeman, College of Letters & Science, 2022) Zignego, Jordan Robert; Chairperson, Graduate Committee: Dennis AigThe photography of Tom Wright, archived at the Dolph Briscoe Center for American History at the University of Texas at Austin, is both art and history. Wright captured many musicians on stage and off at some of the most pivotal moments in both their own careers and in the history of rock music. Although Wright played an integral part with various bands, and produced an amazing body of photographical work in a career that spanned from the 1960s to the 1980s, he has remained unknown. This dissertation argues that Wright belongs in the pantheon of rock photographers as a chronicler and artist; that Wright's photography, and the manner in which it was created, represent the turmoil and conflicts of his era (1960s-1980s) on which he had a specific Anglo-American take as a photographer born in America, but educated in England; that the so-called rock 'n roll life is embodied in Wright's life, including the concept of auto-destruction, that is a primary reason for Wright's lack of recognition; and Wright's relative obscurity is due in large part to his own refusal to work for any publications but to take photographs for their own sake. Wright's photography tells a more nuanced story of rock music. By altering the collectively accepted narrative, his photographs provide a sense of awakening for all and touch on shared memories and how society remembers. Wright's work ultimately offers a more inclusive perspective on how photographs affect both memory and accepted history.Item We: stitching together communities and the self master of fine arts exhibition and analysis(Montana State University - Bozeman, College of Arts & Architecture, 2022) Yonke, Angela Jean; Chairperson, Graduate Committee: Jeremy HatchIn this thesis I created work which visualizes the complexity, support, and importance of community, seeing one another's perspectives, and discussed self-care, our disposable culture by using materials which have symbolic meaning, and were relatable to the viewer. I structured the show to encourage play, engagement, and a desire to connect through use of semiotic visuals and simple directions. I took on the role of director and producer to inspire collaboration and connection within the exhibition. I used everyday window screen material to create clothing for participants to try on. The screen was embroidered with yarn to represent different emotional states through fiber mark making and color. People were invited by strategically placed icons and photographic imagery to try on the items and figuratively try on others' thoughts in an attempt to connect, reflect, and associate the skin of a building to their own body and perspectives. Examples of mending on the screens and photographs of my sewing club stitching on each other presented opportunities to talk about repair and valuing of possessions and the self. Using overlapping screens to create moire patterning, I alluded to the power of people to enrich and transform lives when we interact and overlap, and to see others as windows of opportunity. I presented a community knowledge sharing project, with an online archive and individual visual component, for neighbors to learn from each other and build community, my own shared skill being clothing mending. Gallery-goers were welcomed to add to a collaborative embroidery piece, take screen patches home to mend their own screens, and pose for photographs of themselves in the clothing. I witnessed participants positively interacting with the work. I measure its success by the conversations started and reactions shared regarding ideas which this body of work stimulated by attending and bodily experiencing the show. Further evidence can be seen in the online sharing of the work, and continued stories relayed to me. In this thesis paper I intend to delve more deeply into my research, symbolic use of materials, and conceptual basis for the work.Item Documentary photography, climate crisis, and immigration: 'Migrant mother' as a lens to understand contemporary migrant stories(Montana State University - Bozeman, College of Letters & Science, 2021) Leary, Courtney Lynne Burns; Chairperson, Graduate Committee: Mary MurphyPhotographs are an important tool for understanding American culture and have the potential to influence public perception. Documentary photography specifically can often be used to enact social change and facilitate discourse about uncomfortable or difficult topics from both the past and present. Individual photographs can become defining symbols of entire periods of American history. Dorothea Lange's 'Migrant Mother' is one such example. The current research within the fields of History and American Studies regarding photography is mainly centered on how it can be used in museums and how it fits into our understanding of the past. However, it is also important to acknowledge how particular images have influenced our present understanding of America and how images can be used to facilitate conversations that will contribute to social change. As social media and mass media at large become more integrated into our daily lives and we, as consumers of media, become increasingly inundated with painful images the impact of documentary photography is changing. This first part of this thesis examines the history and tradition of documentary photography in America, including Dorothea Lange's contributions to the field and how 'Migrant Mother' impacted and continues to impact people's understanding of the Great Depression through that single photograph. Chapter Two focuses on the relationship between climate crisis and human migration patterns by examining the Dust Bowl era of the 1930s and the current climate crisis, with a focus on border communities. Chapter Three then examines modern examples of documentary photography to understand how today's documentary photographs impact American attitudes and the effect that America's current state of extreme political polarization has on the social power of particular photographs. Specifically, I analyze three examples: the picture taken of a drowned Syrian child migrant who was attempting to reach Greece in 2015, a photograph taken during the summer of 2019 of a migrant father and his young child drowned in the Rio Grande River after attempting to cross the border into the United States, and recent images taken during the US military withdrawal from Afghanistan in August of 2021.Item Calibration and characterization of a VNIR hyperspectral imager for produce monitoring(Montana State University - Bozeman, College of Engineering, 2020) Logan, Riley Donovan; Chairperson, Graduate Committee: Joseph A. Shaw; Joseph A. Shaw was a co-author of the article, 'Measuring the polarization response of a VNIR hyperspectral imager' in the journal 'SPIE proceedings' which is contained within this thesis.; Bryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, and Joseph A. Shaw were co-authors of the article, 'Hyperspectral imaging and machine learning for monitoring produce ripeness' in the journal 'SPIE proceedings' which is contained within this thesis.Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process of characterizing and calibrating a visible near-infrared (VNIR) hyperspectral imager for obtaining accurate images of produce to be used in machine learning algorithms for analysis. In this work, many calibrations and characterization are outlined, including: a radiance calibration, the process of calculating reflectance, pixel uniformity and image stability testing, spectral characterization, illumination source analysis, and measurement of the polarization response. The images obtained by the calibrated hyperspectral imager were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for Yukon Gold potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using red green blue (RGB) images, full-spectrum hyperspectral images, and the wavelengths selected by the genetic algorithm feature selection method. Preliminary data from these analyses show promising results at accurately classifying produce age. The genetic algorithm feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.Item Utilizing distributions of variable influence for feature selection in hyperspectral images(Montana State University - Bozeman, College of Engineering, 2019) Walton, Neil Stewart; Chairperson, Graduate Committee: John SheppardOptical sensing has been applied as an important tool in many different domains. Specifically, hyperspectral imaging has enjoyed success in a variety of tasks ranging from plant species classification to ripeness evaluation in produce. Although effective, hyperspectral imaging can be prohibitively expensive to deploy at scale. In the first half of this thesis, we develop a method to assist in designing a low-cost multispectral imager for produce monitoring by using a genetic algorithm (GA) that simultaneously selects a subset of informative wavelengths and identifies effective filter bandwidths for such an imager. Instead of selecting the single fittest member of the final population as our solution, we fit a univariate Gaussian mixture model to a histogram of the overall GA population, selecting the wavelengths associated with the peaks of the distributions as our solution. By evaluating the entire population, rather than a single solution, we are also able to specify filter bandwidths by calculating the standard deviations of the Gaussian distributions and computing the full-width at half-maximum values. In our experiments, we find that this novel histogram-based method for feature selection is effective when compared to both the standard GA and partial least squares discriminant analysis. In the second half of this thesis, we investigate how common feature selection frameworks such as feature ranking, forward selection, and backward elimination break down when faced with the multicollinearity present in hyperspectral data. We then propose two novel algorithms, Variable Importance for Distribution-based Feature Selection (VI-DFS) and Layer-wise Relevance Propagation for Distribution-based Feature Selection (LRP-DFS), that make use of variable importance and feature relevance, respectively. Both methods operate by fitting Gaussian mixture models to the plots of their respective scores over the input wavelengths and select the wavelengths associated with the peaks of each Gaussian component. In our experiments, we find that both novel methods outperform variable ranking, forward selection, and backward elimination and are competitive with the genetic algorithm over all datasets considered.Item Convolutional neural networks for multi- and hyper-spectral image classification(Montana State University - Bozeman, College of Engineering, 2019) Senecal, Jacob John; Chairperson, Graduate Committee: John SheppardWhile a great deal of research has been directed towards developing neural network architectures for classifying RGB images, there is a relative dearth of research directed towards developing neural network architectures specifically for multi-spectral and hyper-spectral imagery. The additional spectral information contained in a multi-spectral or hyper-spectral image can be valuable for land management, agriculture and forestry, disaster control, humanitarian relief operations, and environmental monitoring. However, the massive amounts of data generated by a multi-spectral or hyper- spectral instrument make processing this data a challenge. Machine learning and computer vision techniques could automate the analysis process of these rich data sources. With these benefits in mind, we have adapted recent developments in small efficient convolutional neural networks (CNNs), to create a small CNN architecture capable of being trained from scratch to classify 10 band multi-spectral images, using much fewer parameters than popular deep architectures, such as the ResNet or DenseNet architectures. We show that this network provides higher classification accuracy and greater sample efficiency than the same network using RGB images. We also show that it is possible to employ a transfer learning approach and use a network pre-trained on multi-spectral satellite imagery to increase accuracy on a second much smaller multi-spectral dataset, even though the satellite imagery was captured from a much different perspective (high altitude, overhead vs. ground based at close stand-off distance). These results demonstrates that it is possible to train our small network architectures on small multi-spectral datasets and still achieve high classification accuracy. This is significant as labeled hyper-spectral and multi-spectral datasets are generally much smaller than their RGB counterparts. Finally, we approximate a Bayesian version of our CNN architecture using a recent technique known as Monte Carlo dropout. By keeping dropout in place during test time we can perform a Monte Carlo procedure using multiple forward passes of our network to generate a distribution of network outputs which can be used as a measure of uncertainty in the predictions a network is making. Large variance in the network output corresponds to high uncertainty and vice versa. We show that a network that is capable of working with multi-spectral imagery significantly reduces the uncertainty associated with class predictions compared to using RGB images. This analysis reveals that the benefits of an architecture that works effectively with multi-spectral or hyper-spectral imagery extends beyond higher classification accuracy. Multi-spectral and hyper-spectral imagery allows us to be more confident in the predictions that a deep neural network is making.Item Summer camp's color line: racialized landscapes and the struggle for integration, 1890-1950(Montana State University - Bozeman, College of Letters & Science, 2017) Hardin, Amanda Suzanne; Chairperson, Graduate Committee: Billy SmithThough seldom discussed in the larger struggle for African American equality, the ideological and physical exclusion of people of color from outdoor spaces reveals the pervasive, and insidiously widespread nature of white supremacy in the United States. The common historical narrative of the American outdoors focuses on prominent white male figures, such as John Muir or Theodore Roosevelt. This study interrogates the largely unexamined intersections of race and outdoor recreation during the first half of the twentieth century through examining the archival records of three integration-focused summer camps: the Union Settlement Association, the Wiltwyck School for Boys, and Camp Atwater. Analysis of these archives complicates the historiographical concept of 'outdoor recreation' by revealing its connection with white supremacist mentalities and demonstrating the ways in which some people resisted the black-white, urban-nature binary that emerged during this ea. The stories of these camps illuminate more diverse perspectives about the outdoors, and add to an underdeveloped body of research on nonwhite perspectives about recreating in 'natural' environments. By centering these marginalized voices, this scholarship will contribute to future research about similar topics.Item Coping with the landscape: an aesthetic analysis of the intermediate zone(Montana State University - Bozeman, College of Arts & Architecture, 2018) Parker, Ryan Keith; Chairperson, Graduate Committee: Jim ZimpelNumerous studies have been conducted into the aesthetics of landscape, both through objects (sculptures and installations) and through pictorial devices (painting, printmaking, photography, etc.). The fact being that, as long as the horizon line is interrupted these studies by artists will continue in hope of understanding and changing their own reality. Aligning with the history of the photographic land survey, the emphasis of this work is to direct the reading of landscapes towards an aesthetic analysis of the modern mobile landscape. Considering the accumulation of capital as the driving force of the aesthetic change in the landscape, this analysis will focus on the geography of the highest concentration of visible indicators, the intermediate zone. Within this transitional space, as is similarly true with ecological systems, the highest concentration for diversity has the ability to manifest at the edges of converging zones, due to the overlapping of multiple systems in one geographic locality. Accumulation of indicators, both those failing in the system and those entering the system will be present. Recognizing that this survey considers the use and misuse of utilitarian objects and architecture as a method of evaluating time, purpose, and relative availably to the general population, it will present an argument for the intentional denial of the legibility for this landscape, leading to a further lack of understanding within the general population. This result will further lead to the alienation of the population from its landscape.Item Hyper-spectral microscope: auto-focusing(Montana State University - Bozeman, College of Engineering, 2018) Lozano, Kora Michelle; Chairperson, Graduate Committee: Ross K. SniderThis thesis is part of a larger project to develop a hyper-spectral microscope, to be used to find the optimal growing conditions for human inducible pluripotent stem cells. The hyper-spectral microscope is being developed by the Department of Chemistry and Biochemistry at Montana State University (MSU). Specifically, the hyper-spectral microscope is being developed to aide in live cell imaging, reduce cell stress from laser excitation, increase the number of markers possible at once, and keep costs down compared to non-hyper-spectral set-ups of similar capability. To the knowledge of those involved in this project it is the first of its kind. The scope of this thesis centers on implementing an auto-focusing algorithm for the hyper-spectral imager.Item Photographic images on ceramic surfaces(Montana State University - Bozeman, 1971) Fuglestad, Renee E.