Moire Fringe Center Determination Using Artificial Neural Network

Moire 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 moire patterns. In a recent application of using circular gratings moire pattern, researchers developed graphical analysis method to determine the in-plane (2-D) displacement change between the two circular gratings by analyzing the moire pattern change. In this work, an artificial neural network approach was proposed to detect and locate moire fringe centers of circular gratings without image preprocessing and curve fitting. The intensity values in columns of the transformed circular moire pattern were extracted as the input to the neural network. Moire 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 moire fringe centers. The result showed that the neural network approach is applicable in moire fringe centers determination and its feasibility in automating moire pattern analysis with further improvement.