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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...
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 ...
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 ...