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Confocal microscopy imaging analysis of plant morphodynamics
(University of Missouri--Columbia, 2010)
Pollen tubes are delivery mechanisms in a plant's reproductive cycle. Morphodynamic analysis of these microscopic structures provide biologists with insight into the inner workings of these structures. This thesis presents ...
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 ...
Machine learning methods for 3D object classification and segmentation
(University of Missouri--Columbia, 2018)
Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. ...
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 for computers. My work...
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 ...
Image matching and image super-resolution via deep learning
(University of Missouri--Columbia, 2018)
The advancement of 3D depth sensors, such as LIDAR (Light Detection And Ranging) scanners, has provided an effective alternative to traditional CAD-based and image-based approaches for 3D modeling. The output of the 3D ...