Faster region-based convolutional neural network method for estimating parameters from Newton's rings

Chen-Chen Ji; Ming-Feng Lu; Jin-Min Wu; Zhen Guo; Feng Zhang; Ran Tao

doi:10.1117/12.2525807

21 June 2019 Faster region-based convolutional neural network method for estimating parameters from Newton's rings

Chen-Chen Ji, Ming-Feng Lu, Jin-Min Wu, Zhen Guo, Feng Zhang, Ran Tao

Proceedings Volume 11057, Modeling Aspects in Optical Metrology VII; 110570X (2019) https://doi.org/10.1117/12.2525807
Event: SPIE Optical Metrology, 2019, Munich, Germany

Abstract

Newton’s rings are the fringe patterns of quadratic phase, the curvature radius of optical components can be obtained from the coefficients of quadratic phase. Usually, the coordinate transformation method has been used to the curvature radius, however, the first step of the algorithm is to find the center of the circular fringes. In recent years, deep learning, especially the deep convolutional neural networks (CNNs), has achieved remarkable successes in object detection task. In this work, an new approach based on the Faster region-based convolutional neural network (Faster R-CNN) is proposed to estimate the rings’ center. Once the rings’ center has been detected, the squared distance from each pixel to the rings’ center is calculated, the two-dimensional pattern is transformed into a one-dimensional signal by coordinate transformation, fast Fourier transform of the spectrum reveals the periodicity of the one-dimensional fringe profile, thus enabling the calculation of the unknown surface curvature radius. The effectiveness of this method is demonstrated by the simulation and actual images.

Conference Presentation

Citation Download Citation

Chen-Chen Ji, Ming-Feng Lu, Jin-Min Wu, Zhen Guo, Feng Zhang, and Ran Tao "Faster region-based convolutional neural network method for estimating parameters from Newton's rings", Proc. SPIE 11057, Modeling Aspects in Optical Metrology VII, 110570X (21 June 2019); https://doi.org/10.1117/12.2525807

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