Artificial neural network approach for moire fringe center determination

The moire effect has been used in high-accuracy positioning and alignment systems for decades. Various methods have been proposed to identify and locate moire fringes in order to relate the pattern information to dimensional and displacement measurement. These methods can be broadly categorized into manual interpretation based on human knowledge and image processing based on computational algorithms. An artificial neural network (ANN) is proposed to locate moire fringe centers within circular grating moire patterns. This ANN approach aims to mimic human decision making by eliminating complex mathematical computations or time-consuming image processing algorithms in moire fringe recognition. A feed-forward backpropagation ANN architecture was adopted in this work. Parametric studies were performed to optimize the ANN architecture. The finalized ANN approach was able to determine the location of the fringe centers with average deviations of 3.167 pixels out of 200 pixels (approximate to 1.6%) and 6.166 pixels out of 200 pixels (approximate to 3.1%) for real moire patterns that lie within and outside the training intervals, respectively. In addition, a reduction of 43.4% in the computational time was reported using the ANN approach. Finally, the applicability of the ANN approach for moire fringe center determination was confirmed. (C) 2015 SPIE and IS&T