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Gauging materials’ physical properties from video

25 May 2015

'Visual microphone' technology developed by researchers at MIT could lead to non-invasive identification of an object's structural defects.

Illustration: Christine Daniloff/MIT
Illustration: Christine Daniloff/MIT

Last summer, MIT researchers published a paper describing an algorithm that can recover intelligible speech from the analysis of the minute vibrations of objects in video captured through soundproof glass.

Next month, at the Conference on Computer Vision and Pattern Recognition in Boston, researchers from the same groups will describe how the technique can be adapted to infer material properties of physical objects, such as stiffness and weight, from video.

The technique could have application in the field of non-destructive testing, or determining materials’ physical properties without extracting samples from them or subjecting them to damaging physical tests. It might be possible, for instance, to identify structural defects in an aircraft's wing by analysing video of its vibration during flight.

The researchers applied their technique to two different types of object. One was rods of fibreglass, wood, and metal; the other, fabrics draped over a line.

In the case of the rods, they used a range of frequencies from a nearby loudspeaker to produce vibrations. And since the vibrational frequencies of stiff materials are high, they also used a high-speed camera — as they did in some of the visual-microphone work — to capture the video.

The fabrics, however, were flexible enough that the ordinary circulation of air in a closed room was enough to produce detectable vibrations. And the vibration rates were low enough to be measured using an ordinary digital camera.

Although its movement may be undetectable to the human eye, a vibrating object usually vibrates at several frequencies at the same time. A given object’s preferred frequencies, and the varying strength of its vibrations at those frequencies, produce a unique pattern, which a variation on the visual-microphone algorithm was able to extract.

The researchers then used machine learning to find correlations between those vibrational patterns and measurements of the objects’ material properties. The correlations they found provided good estimates of the elasticity of the bars and of the stiffness and weight per unit area of the fabrics.

Moreover, aberrations or discontinuities in an object’s typical vibrational patterns could indicate a defect in its structure. Identifying those types of correlations is the subject of the researchers’ ongoing work.

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