The measurement of the diopters of spectacle lenses using an artificial neural network is proposed in this paper. A diopter is measured by obtaining the distances between the spots imaged by a charge-coupled device (CCD) during a Hartmann test. Backpropagation (BP) and a radial basis function (RBF) algorithm are applied to train the BP and RBF neural networks, respectively. A set of − 20 D to 20 D spectacle lenses is used to test the proposed method. When comparing the diopter measurement results of the RBF neural network with those of the BP neural network, the former exhibited higher stability and lower errors. In addition, the errors of diopter measurement are not against the system error measured by an auto-focimeter due to the increase of curvature of spectacle lenses. These results indicated that RBF neural network has better performance in diopter detection, and it can overcome the system error caused by the change of lens curvature in auto-focimeter measurement.
In traditional focimeter measurements, the lens cannot completely coincide with the diaphragm owing to the change of radius, resulting in an increase in the power measurement error with an increase in the lens power. We proposed a method, using the SVM machine learning algorithm, to restore the measurement of the focimeter, using a lens power data set obtained from lens features, obtained through an automatic acquisition system. Total up to 83 groups of single focus lenses with refractive indices of 1.56 and 1.60, ranging from -10 m-1 to + 8 m-1 every 0.25 m-1 and -10.5 m-1 to -15 m-1 every 0.5 m-1, were used for lens image acquisition. The experimental results show that the ten-fold average F1 score of the classification under this method is 100%. The test lens power can be accurately identified and the measurement error can be overcome.
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