Tree topology estimation
Tree-like structures are fundamental in nature, and it is often useful to reconstruct the topology of a tree-what connects to what-from a two-dimensional image of it. However, the projected branches often cross in the image: the tree projects to a planar graph, and the inverse problem of reconstructing the topology of the tree from that of the graph is ill-posed. We regularize this problem with a generative, parametric tree-growth model. Under this model, reconstruction is possible in linear time if one knows the direction of each edge in the graph-which edge endpoint is closer to the root of the tree-but becomes NP-hard if the directions are not known. For the latter case, we present a heuristic search algorithm to estimate the most likely topology of a rooted, three-dimensional tree from a single two-dimensional image. Experimental results on retinal vessel, plant root, and synthetic tree data sets show that our methodology is both accurate and efficient.
Duke Scholars
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Related Subject Headings
- Trees
- Stochastic Processes
- Retinal Vessels
- Lightning
- Imaging, Three-Dimensional
- Humans
- Databases, Factual
- Artificial Intelligence & Image Processing
- Artificial Intelligence
- Algorithms
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Trees
- Stochastic Processes
- Retinal Vessels
- Lightning
- Imaging, Three-Dimensional
- Humans
- Databases, Factual
- Artificial Intelligence & Image Processing
- Artificial Intelligence
- Algorithms