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Browsing by Author "Sreedharan, Sarath, committee member"

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    SMOKE+: a video dataset for automated fine-grained assessment of smoke opacity
    (Colorado State University. Libraries, 2024) Seefried, Ethan, author; Blanchard, Nathaniel, advisor; Sreedharan, Sarath, committee member; Roberts, Jacob, committee member
    Computer vision has traditionally faced difficulties when applied to amorphous objects like smoke, owing to their ever-changing shape, texture, and dependence on background conditions. While recent advancements have enabled simple tasks such as smoke detection and basic classification (black or white), quantitative opacity estimation in line with the assessments made by certified professionals remains unexplored. To address this gap, I introduce the SMOKE+ dataset, which features opacity labels verified by three certified experts. My dataset encompasses five distinct testing days, two data collection sites in different regions, and a total of 13,632 labeled clips. Leveraging this data, we develop a state-of-the-art smoke opacity estimation method that employs a small number of Residual 3D blocks for efficient opacity estimation. Additionally I explore the use of MAMBA blocks in a video based architecture, exploiting their ability to handle spatial and temporal data in a linear fashion. Techniques developed during the SMOKE+ dataset creation were then refined and applied to a new dataset titled CSU101, designed for educational use in Computer Vision. In the future I intend to expand further into synthetic data, incorporating techniques into Unreal Engine or Unity to add accurate opacity labels.
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    Using eye gaze to automatically identify familiarity
    (Colorado State University. Libraries, 2024) Castillon, Iliana, author; Blanchard, Nathaniel, advisor; Sreedharan, Sarath, committee member; Cleary, Anne M., committee member
    Understanding internal cognitive states, such as the sensation of familiarity, is crucial not only in the realm of human perception but also in enhancing interactions with artificial intelligence. One such state is the experience of familiarity, a fundamental aspect of human perception that often manifests as an intuitive recognition of faces or places. Automatically identifying cognitive experiences could pave the way for more nuance in human-AI interaction. While other works have shown the feasibility of automatically identifying other internal cognitive states like mind wandering using eye gaze features, the automatic detection of familiarity remains largely unexplored. In this work, we employed a paradigm from cognitive psychology to induce feelings of familiarity. Then, we trained machine learning models to automatically detect familiarity using eye gaze measurements, both in experiments with traditional computer use (e.g., eye tracker attached to monitor) and in virtual reality settings, in a participant independent manner. Familiarity was detected with a Cohen's kappa value, a measurement of accuracy corrected for random guessing, of 0.22 and 0.21, respectively. This work showcases the feasibility of automatically identifying feelings of familiarity and opens the door to exploring automated familiarity detection in other contexts, such as students engaged with a learning task while interacting with an intelligent tutoring system.