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Obtaining subpixel level cutting tool displacements from video using a CNN architecture


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1 Indian Institute of Technology Kanpur, Kanpur, India, India
     

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To register motion from video of vibrating tools, acquisition must ensure that motion is spatially and temporally resolved. However, since tools often vibrate with subpixel level motion, and since cameras often trade speed for resolution, if acquisition is to respect the Nyquist limit to avoid temporal aliasing, then the spatial resolution is often not sufficient to detect small cutting tool motion. To address this problem, this paper shows for the first time that subpixel level tool motion can be inferred instead by using convolution neural networks. We train our model on a database using the phase-based optical flow scheme that is a subpixel level motion registration algorithm. Our model is shown to be capable of detecting small motion correctly. Though the frequency of vibration estimated from the registered motion is correct, further work is necessary on fine tuning model architecture to fix the errors observed in the estimation of damping.

Keywords

Convolution Neural Network, Vibrations, Phase-Based Optical Flow, Visual Vibrometry.
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  • Obtaining subpixel level cutting tool displacements from video using a CNN architecture

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Authors

Varun Raizada
Indian Institute of Technology Kanpur, Kanpur, India, India
Mohit Law
Indian Institute of Technology Kanpur, Kanpur, India, India

Abstract


To register motion from video of vibrating tools, acquisition must ensure that motion is spatially and temporally resolved. However, since tools often vibrate with subpixel level motion, and since cameras often trade speed for resolution, if acquisition is to respect the Nyquist limit to avoid temporal aliasing, then the spatial resolution is often not sufficient to detect small cutting tool motion. To address this problem, this paper shows for the first time that subpixel level tool motion can be inferred instead by using convolution neural networks. We train our model on a database using the phase-based optical flow scheme that is a subpixel level motion registration algorithm. Our model is shown to be capable of detecting small motion correctly. Though the frequency of vibration estimated from the registered motion is correct, further work is necessary on fine tuning model architecture to fix the errors observed in the estimation of damping.

Keywords


Convolution Neural Network, Vibrations, Phase-Based Optical Flow, Visual Vibrometry.

References