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Visual Vibrometry: Estimating Material Properties from Small Motions in Video

Davis, Abe and Bouman, Katherine L. and Chen, Justin G. and Rubinstein, Michael and Büyüköztürk, Oral and Durand, Frédo and Freeman, William T. (2017) Visual Vibrometry: Estimating Material Properties from Small Motions in Video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (4). pp. 732-745. ISSN 0162-8828. https://resolver.caltech.edu/CaltechAUTHORS:20190405-140148963

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Abstract

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tpami.2016.2622271DOIArticle
ORCID:
AuthorORCID
Bouman, Katherine L.0000-0003-0077-4367
Freeman, William T.0000-0002-2231-7995
Additional Information:© 2017 IEEE. Manuscript received 27 Mar. 2016; revised 20 July 2016; accepted 12 Sept. 2016. Date of publication 31 Oct. 2016; date of current version 2 Mar. 2017. Recommended for acceptance by K. Grauman, A. Torralba, E. Learned-Miller, and A. Zisserman. Dr. Dirk Smit of Shell Research proposed to us the analysis of small displacements for structural health monitoring. We would also like to thank Neal Wadhwa, Gautham J. Mysore, and Danny M. Kaufman. This work was supported by US National Science Foundation Robust Intelligence 1212849 Reconstructive Recognition, NSF CGV-1111415, Shell Research, and Qatar Computing Research Institute. A. Davis and K. Bouman were partially supported by US National Science Foundation GRFP fellowships.
Funders:
Funding AgencyGrant Number
NSFIIS-1212849
NSFCGV-1111415
Shell ResearchUNSPECIFIED
Qatar Computing Research InstituteUNSPECIFIED
NSF Graduate Research FellowshipUNSPECIFIED
Subject Keywords:Material properties, vibration, small motion, computational photography, computational imaging
Issue or Number:4
Record Number:CaltechAUTHORS:20190405-140148963
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190405-140148963
Official Citation:A. Davis et al., "Visual Vibrometry: Estimating Material Properties from Small Motions in Video," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 732-745, 1 April 2017. doi: 10.1109/TPAMI.2016.2622271
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94506
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:05 Apr 2019 21:59
Last Modified:03 Oct 2019 21:04

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