Published April 2017 | Version public
Journal Article

Visual Vibrometry: Estimating Material Properties from Small Motions in Video

  • 1. ROR icon Massachusetts Institute of Technology
  • 2. ROR icon Microsoft (United States)

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.

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.

Additional details

Identifiers

Eprint ID
94506
Resolver ID
CaltechAUTHORS:20190405-140148963

Funding

NSF
IIS-1212849
NSF
CGV-1111415
Shell Research
Qatar Computing Research Institute
NSF Graduate Research Fellowship

Dates

Created
2019-04-05
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Astronomy Department