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Augmenting biological pathway extraction with synthetic data and active learning
(University of Missouri--Columbia, 2022)
The corpus of biomedical literature is growing rapidly as many papers are recorded in PubMed every day. These papers often contain high-quality biological pathways in their figures/text, which are great resources for ...
Deep learning methods for 360 monocular depth estimation and point cloud semantic segmentation
(University of Missouri--Columbia, 2022)
Monocular depth estimation and point cloud segmentation are essential tasks for 3D scene understanding in computer vision. Depth estimation for omnidirectional images is challenging due to the spherical distortion issue and the availability of large...
Tensor completion methods with provable consistency and fairness guarantees for recommender systems
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 8/1/2024] This thesis introduces novel consistency-based approaches for solving nonnegative/ positive matrix and tensor completion problems in the context of recommender systems. The proposed framework ...
Deep learning-based solutions for electron microscopy image analysis
(University of Missouri--Columbia, 2023)
Electron microscopy (EM) enables capturing high resolution images of very small structures in biological and non-biological specimens such as membrane proteins, viruses, subcellular structures, nanoparticles, or material ...
Single and multi-object video tracking using local and deep architectures
(University of Missouri--Columbia, 2022)
Moving object tracking is a fundamental computer vision task with a wide variety of real-life applications ranging from surveillance and autonomous systems to biomedical video analysis. A robust, accurate, scalable, and ...
Custom templates based heterogeneous resource allocation for data-intensive applications
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] The increase of data-intensive applications in science and engineering fields (i.e., bioinformatics, cybermanufacturing) demand the use of ...
Multi-modal and multi-dimensional biomedical image data analysis using deep learning
(University of Missouri--Columbia, 2023)
There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early ...
Application of deep reinforcement learning for battery design
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The conventional material research and development are mainly driven by human intuition, labor, and manual decision. It is ineffective and inefficient. ...
Deep learning and DCT-based hand-crafted features for computer vision tasks
(University of Missouri--Columbia, 2022)
Feature extraction and matching are critical components for many computer vision tasks including camera pose estimation, 3D reconstruction, simultaneous localization and mapping (SLAM), and object tracking, etc. Features ...
Deep heterogeneous superpixel neural networks for image analysis and feature extraction
(University of Missouri--Columbia, 2021)
Lately, deep convolutional neural networks are rapidly transforming and enhancing computer vision accuracy and performance, and pursuing higher-level and interpretable object recognition. Superpixel-based methodologies ...