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Visual Vibrometry: Estimating Material Properties from Small Motions in Video

Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Oral Büyüköztürk, Frédo Durand, William T. Freeman

Year
2016
Citations
78

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.

Keywords

Artificial intelligenceComputer visionComputer scienceMotion estimationMotion (physics)RoboticsSet (abstract data type)Frame rateVibrationObject (grammar)

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