Doctor of Philosophy (Ph.D.), University of Dayton, 2024, Computer Science

Evaluating anomaly factors in images is a pivotal element in advancing the robustness of image processing techniques, particularly under adverse and dynamic conditions. This thesis presents a comprehensive investigation into anomaly factors, focusing on two major evaluations: anomaly addition and anomaly removal. In the first evaluation, anomaly addition, we assess the resilience of computer vision frameworks in real-world scenarios. Specifically, this involves studying the performance of object detection algorithms in adverse weather conditions, such as fog, rain, snow, and sun flare, which pose significant challenges to autonomous vehicle technologies. Our methodology includes calculating Intersection over Union (IoU) to measure bounding box overlap between model predictions and ground truth labels, allowing for an accurate assessment of true positives (TP), false positives (FP), and false negatives (FN) across multiple classes. We use performance metrics such as class accuracy, precision, recall, F1 score, and average accuracy to provide a comprehensive view of model robustness. Through ablation studies and dual-modality architecture analysis, the impact of these anomalies on traffic monitoring, vehicle tracking, and object detection is thoroughly examined. The findings underscore the limitations of algorithms trained under clear weather conditions and emphasize the need for more adaptive systems to ensure safety and efficiency in intelligent transportation technologies. The second evaluation, anomaly removal, explores the effectiveness of image inpainting techniques in removing undesired elements, such as photobombing, from images. A benchmarking study was conducted to compare state-of-the-art inpainting methods on a dataset of over 300 images. Using performance metrics like PSNR, SSIM, and FID, the results reveal both the strengths and limitations of current techniques in restoring images with varying levels of complexity. Our evaluation provides a valuab (open full item for complete abstract)

Committee: Tam Nguyen (Committee Chair); Ju Shen (Committee Member); Vijayan Asari (Committee Member); James Buckley (Committee Member) Subjects: Artificial Intelligence; Computer Science

DMA, University of Cincinnati, 2011, College-Conservatory of Music: Piano

As a practical performance guide, this document identifies performance issues for Franz Schubert's Piano Sonata in B-flat Major, D. 960, and proposes possible solutions. The introduction provides a brief historical background of Schubert's piano sonatas in general and the B-flat Sonata in particular. Chapter one demonstrates how a harmonic analysis of the B-flat Sonata can inform such performance issues as timing, pacing, and tone color. Chapter two concerns performance practice issues related to playing this sonata, such as tempos, repeat signs, dynamics, pedaling, rhythm, and memorization. Through a comparison of two performing editions and a critical edition, chapter three suggests the best editions for the B-flat Sonata. Consequently, I hope this document will become a practical source to help pianists interpret and perform Schubert's B-flat Sonata.

Committee: bruce mcclung PhD (Committee Chair); Awadagin Pratt (Committee Member); Elizabeth Pridonoff MM (Committee Member) Subjects: Music