Moiré fringe center determination using artificial neural network

W. H. Woo; K. S. Yen

doi:10.1117/12.2197089

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6 July 2015 Moiré fringe center determination using artificial neural network

W. H. Woo, K. S. Yen

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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96312B (2015) https://doi.org/10.1117/12.2197089
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

Moiré methods are commonly used in various engineering metrological practices such as deformation measurements and surface topography. In the past, most of the applications required human intervention in fringe pattern analysis and image processing development to analyze the moiré patterns. In a recent application of using circular gratings moiré pattern, researchers developed graphical analysis method to determine the in-plane (2-D) displacement change between the two circular gratings by analyzing the moiré pattern change. In this work, an artificial neural network approach was proposed to detect and locate moiré fringe centers of circular gratings without image preprocessing and curve fitting. The intensity values in columns of the transformed circular moiré pattern were extracted as the input to the neural network. Moiré fringe centers extracted using graphical analysis method were used as the target for the neural network training. The neural network produced reasonably accurate output with an average mean error of an average mean error of less than 1 unit pixel with standard deviation of less than 4 unit pixels in determining the location of the moiré fringe centers. The result showed that the neural network approach is applicable in moiré fringe centers determination and its feasibility in automating moiré pattern analysis with further improvement.

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W. H. Woo and K. S. Yen "Moiré fringe center determination using artificial neural network", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96312B (6 July 2015); https://doi.org/10.1117/12.2197089

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