Date of Award
5-14-2024
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious segments or cycles that need to be removed. We propose a dynamic programming algorithm which seeks to recover directed trees from these noisy skeletons. Our method recovers trees by removing edges and duplicating nodes while adhering to edge-label constraints. Our algorithm proceeds by iteratively merging graph nodes, such that the solution on the original graph can be obtained from those on the contracted graphs. Furthermore, we show that our method can recover trees in many settings, such as from rice root images, retinal fundus images, 3D corn root images, and 3D grapevine images.
Language
English (en)
Chair
Tao Ju