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Conversation understanding and realistic artificial crash data generation with deep learning
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] This dissertation focuses on conversation understanding and realistic crash data generation with deep learning. Conversation understanding includes conversational outcome, formality, and politeness ...
Data-driven 3D shape modeling
(University of Missouri--Columbia, 2010)
3D shape modeling is essential for computer to understand our real world. So far, 3D shaping modeling is still an open issue. There are too much raw data around, but there is no uniform or standard way to translate them ...
Relative depth estimation from single monocular images with deep convolutional network
(University of Missouri--Columbia, 2017)
Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice. This thesis addresses the problem of depth estimation from single monocular ...
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 ...
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 ...
Protein tertiary structure prediction and refinement using deep learning
(University of Missouri--Columbia, 2022)
Building the high-quality structure of a protein from its amino acid sequence has important applications in protein engineering and drug design. The problem of accurate protein three-dimensional structure prediction from ...