Knowledge Vault 5 /10 - CVPR 2015
Visual Vibrometry: Estimating Material Properties From Small Motion in Video
Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Fr├ędo Durand, William T. Freeman
< Resume Image >

Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef vibration fill:#f9d4d4, font-weight:bold, font-size:14px classDef computer_vision fill:#d4f9d4, font-weight:bold, font-size:14px classDef analysis fill:#d4d4f9, font-weight:bold, font-size:14px classDef experiments fill:#f9f9d4, font-weight:bold, font-size:14px classDef applications fill:#f9d4f9, font-weight:bold, font-size:14px A[Visual Vibrometry: Estimating
Material Properties From
Small Motion in
Video] --> B[Objects vibrate at resonant
frequencies and modes 1] A --> C[Computer vision recovers subtle
vibrations from videos 2] C --> D[Relates motion frequencies to
resonant frequencies 3] D --> E[Estimates material properties
like stiffness 4] B --> F[Small-scale deformation around
objects rest state 5] F --> G[Simple to analyze via
linear modal analysis 6] A --> H[Non-destructive testing assesses
physical properties without damage 7] H --> I[Video enables trading temporal
for spatial resolution 8] C --> J[Ubiquity of video allows
data-driven approaches 9] J --> K[Applications in material
recognition 10] A --> L[Analysis uses global power
spectrum of motion 11] L --> M[Spikes in spectrum indicate
vibration modes/shapes 12] M --> N[Mode shapes relate motion
at different points 13] B --> O[Resonant frequencies are global,
viewpoint-invariant properties 14] A --> P[Modal analysis: frequency depends
on geometry and material 15] P --> Q[Spectrum ambiguity resolved by
known geometry information 16] A --> R[Local pixel motion spectra
calculated and visualized 17] R --> S[Spectra averaged for global
power spectrum 18] A --> T[Experiment 1: Clamped rod
with known geometry 19] T --> U[Rods fundamental frequency
encodes material properties 20] U --> V[Estimated elasticity matches
ground truth measurements 21] A --> W[Experiment 2: Hanging fabric
with similar geometry 22] W --> X[Small fabric vibrations relate
to physical properties 23] X --> Y[Power spectra from imperceptible
ambient fabric motion 24] Y --> Z[Similar geometry enables
meaningful spectra comparison 25] Z --> AA[Regression predicts fabric
material properties 26] AA --> AB[Accurate predictions of weight
and stiffness trends 27] AB --> AC[Features invariant to camera
and viewpoint 28] C --> AD[Cameras promising for vibrational
analysis vs equipment 29] AD --> AE[Enables new computer
vision applications 30] class A,B,F,O vibration class C,I,J,AD,AE computer_vision class D,E,G,L,M,N,P,Q,R,S analysis class T,U,V,W,X,Y,Z,AA,AB,AC experiments class H,K applications

Resume:

1.- Objects vibrate at preferred resonant frequencies and modes, often imperceptibly.

2.- Recent computer vision work can recover subtle vibrations from regular videos.

3.- Relating temporal motion frequencies to an object's resonant frequencies enables estimating its vibration modes.

4.- Vibration modes + object geometry information allows estimating material properties like stiffness.

5.- Motion considered is very small-scale deformation around an object's rest state.

6.- Small deformation scale makes vibrations difficult to see but simple to analyze via linear modal analysis.

7.- Drawing from non-destructive testing field, which assesses physical properties of structures without damage.

8.- Typical vibrational analysis uses contact sensors or laser vibrometers. Video enables trading temporal resolution for spatial resolution.

9.- Ubiquity of video allows data-driven approaches impractical with specialized vibrational analysis equipment.

10.- Estimating material properties from video has applications in material recognition.

11.- Analysis uses a global power spectrum summarizing temporal frequencies of motion in the video.

12.- Objects vibrate at preferred resonant frequencies, visible as spikes in the motion spectrum. Each spike is a vibration mode/shape.

13.- Vibration mode shapes relate motion at different object points - opposite moving points have opposite phase.

14.- Resonant frequencies are global object properties, largely invariant to viewpoint changes.

15.- Modal analysis: resonant frequency pattern depends on object geometry, scaled by material properties.

16.- Motion spectrum gives ambiguous combination of structural and material properties, resolved by known geometry information.

17.- Local pixel motion spectra calculated, phase and amplitude visualized to see mode shapes.

18.- Spectra are powered and averaged to get global constructive power spectrum used to estimate material properties.

19.- Experiment 1: Estimating material properties of clamped rod with known geometry from high-speed video.

20.- Rod's fundamental frequency encodes material properties. Known geometry allows isolating material properties' effect.

21.- Rod motion extracted, power spectrum calculated, fundamental frequency spike identified using expected resonance ratios and mode shapes.

22.- Estimated rod elasticity closely matches ground truth measurements for various materials and lengths.

23.- Experiment 2: Estimating material properties of hanging fabric with unknown but similar geometry from regular video.

24.- Small fabric vibrations governed by simpler relationships than large wind-blown deformations, relate to physical properties.

25.- Power spectra calculated from imperceptible ambient fabric motion recorded with regular camera.

26.- Similar fabric geometry enables meaningful spectra comparison, with trends apparent across samples.

27.- Regression model predicts fabric material properties from power spectrum features.

28.- Accurate predictions of area weight and stiffness trends from both sound-excited and passive fabric motion.

29.- Features are largely invariant to camera and viewpoint, outperform prior work.

30.- Cameras offer promise for vibrational analysis vs specialized equipment, enables new computer vision applications.

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