Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm

doi:10.3390/mi14061163

CIOMP OpenIR

	Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm
	Z. Wang, T. Wang, Y. Yang, X. Mi and J. Wang
	2023
发表期刊	Micromachines
ISSN	2072666X
卷号	14 期号:6
摘要	Quantifying free-form surfaces using differential confocal microscopy can be challenging, as it requires balancing accuracy and efficiency. When the axial scanning mechanism involves sloshing and the measured surface has a finite slope, traditional linear fitting can introduce significant errors. This study introduces a compensation strategy based on Pearson’s correlation coefficient to effectively reduce measurement errors. Additionally, a fast-matching algorithm based on peak clustering was proposed to meet real-time requirements for non-contact probes. To validate the effectiveness of the compensation strategy and matching algorithm, detailed simulations and physical experiments were conducted. The results showed that for a numerical aperture of 0.4 and a depth of slope © 2023 by the authors.
DOI	10.3390/mi14061163
URL	查看原文
收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68003
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. Wang, T. Wang, Y. Yang, X. Mi and J. Wang. Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm[J]. Micromachines,2023,14(6).
APA	Z. Wang, T. Wang, Y. Yang, X. Mi and J. Wang.(2023).Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm.Micromachines,14(6).
MLA	Z. Wang, T. Wang, Y. Yang, X. Mi and J. Wang."Differential Confocal Optical Probes with Optimized Detection Efficiency and Pearson Correlation Coefficient Strategy Based on the Peak-Clustering Algorithm".Micromachines 14.6(2023).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Differential Confoca（9803KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载