Seeing Tree Structure from Vibration
Name
1809.05067.pdf
Description
Accepted version
Size
8.41 MB
Format
Adobe PDF
Checksum (MD5)
333a5f85ba02ac2801f995b84b2909a1
Author(s)
Xue, Tianfan
Wu, Jiajun
Zhang, Zhoutong
Zhang, Chengkai
Tenenbaum, Joshua B
Freeman, William T
Date Issued
October 2018
Journal
European Conference on Computer Vision
Publisher
Springer International Publishing
Citation
Xue, Tianfan et al. "Seeing Tree Structure from Vibration." European Conference on Computer Vision, September 2018, Munich, Germany, Springer International Publishing, October 2018. © Springer Nature Switzerland AG 2018.
Version
Author's final manuscript
Abstract
Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-01240-3_46
