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Volumetric medical image segmentation with deep learning pipelines
(University of Missouri--Columbia, 2020)
Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the ...
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...
Advance : adversarial collaborative learning for detection and verification of artificially created examples
(University of Missouri--Columbia, 2023)
Adversarial learning methods have gained significant popularity in generating deceptive yet convincingly authentic data. While these techniques have proven beneficial for advancing artificial intelligence, they also give ...
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 ...
GPU implementation of video analytics algorithms for aerial imaging
(University of Missouri--Columbia, 2023)
This work examines several algorithms that together make up parts of an image processing pipeline called Video Mosaicing and Summarization (VMZ). This pipeline takes as input geospatial or biomedical videos and produces ...
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 ...
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 ...
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 ...
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 ...
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. ...
Information fusion for robust detection of scarce features/objects in high resolution electro-optical satellite imagery
(University of Missouri--Columbia, 2021)
We conducted research to develop and test methods to improve the detection of scarce objects in high-resolution electro-optical satellite imagery. We demonstrated improvements through various forms of information and data ...