Overcoming the error of optical power measurement caused by the curvature radius

Abstract

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.

Improved Boundary Support Vector Clustering with Self-Adaption Support

“…Following [13,14], the expected sphere is estimated by a support function, which is defined as a positive scalar function f : R n → R + . Since the support function is constructed by SVs, we estimate it by solving a dual problem in Equation (2), where x i corresponds to the coefficient…”

“…Since each subset X i has N K data samples, the proposed MES requires O(( N K ) 2 ). Then, BABE requires O(k 2 M 2 ) to remove the fake edges, while the iSolver takes O(M 2 2 ) to get β for the support vector function in Equation (3). Therefore, the total time complexity for the training phase of IBSVC is…”

“…Support vector clustering (SVC) has attracted much attention for handling clusters with arbitrary shapes [1,2]. For a better description, support vectors (SVs) with their specific coefficients should generally be collected through excellent model training, which requires a large number of valid training samples and a complex iterative analysis under specific metrics.…”