Seeing Tree Structure from Vibration
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 informa...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
Springer International Publishing
2020
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| Online Access: | https://hdl.handle.net/1721.1/126605 |
| _version_ | 1811079645929406464 |
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| author | Xue, Tianfan Wu, Jiajun Zhang, Zhoutong Zhang, Chengkai Tenenbaum, Joshua B Freeman, William T |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Xue, Tianfan Wu, Jiajun Zhang, Zhoutong Zhang, Chengkai Tenenbaum, Joshua B Freeman, William T |
| author_sort | Xue, Tianfan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:18:22Z |
| format | Book |
| id | mit-1721.1/126605 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:18:22Z |
| publishDate | 2020 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1266052022-09-27T18:35:56Z Seeing Tree Structure from Vibration Xue, Tianfan Wu, Jiajun Zhang, Zhoutong Zhang, Chengkai Tenenbaum, Joshua B Freeman, William T Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. NSF (Grants 1231216, 1212849 and 1447476) ONR MURI (Grant N00014-16-1-2007) 2020-08-14T23:09:52Z 2020-08-14T23:09:52Z 2018-10 2019-05-28T12:08:01Z Book http://purl.org/eprint/type/ConferencePaper 9783030012397 9783030012403 0302-9743 1611-3349 https://hdl.handle.net/1721.1/126605 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. en http://dx.doi.org/10.1007/978-3-030-01240-3_46 European Conference on Computer Vision Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer International Publishing Springer |
| spellingShingle | Xue, Tianfan Wu, Jiajun Zhang, Zhoutong Zhang, Chengkai Tenenbaum, Joshua B Freeman, William T Seeing Tree Structure from Vibration |
| title | Seeing Tree Structure from Vibration |
| title_full | Seeing Tree Structure from Vibration |
| title_fullStr | Seeing Tree Structure from Vibration |
| title_full_unstemmed | Seeing Tree Structure from Vibration |
| title_short | Seeing Tree Structure from Vibration |
| title_sort | seeing tree structure from vibration |
| url | https://hdl.handle.net/1721.1/126605 |
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