训练数据集对光学拉盖尔-高斯模式识别精度影响的研究

IF 1 Q4 OPTICS

Optical Memory and Neural Networks Pub Date : 2023-11-03 DOI:10.3103/S1060992X2305003X

A. V. Bekhterev

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引用次数: 0

摘要

本文研究了具有仿射变换形式的几何失真的拉盖尔和埃尔米特-高斯光学模式的卷积神经网络识别的准确性。分析了训练数据采样对精度的影响。还检验了卷积神经网络识别具有由环境失真引起的几何失真并由仿射变换描述的拉盖尔和埃尔米特-高斯光学模式的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition

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Investigation of the Training Data Set Influence on the Accuracy of the Optical Laguerre-Gaussian Modes Recognition

This paper investigates the accuracy of convolutional neural network recognition of Laguerre and Hermite-Gauss optical modes with geometric distortions in the form of affine transformations. The influence of training data sampling on the accuracy is analyzed. The ability of a convolutional neural network to recognize Laguerre and Hermite-Gaussian optical modes with geometric distortions caused by environmental distortions and described by affine transformations was also examined.

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.