Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the p...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6460
Main Authors: Zhu, Xuedong, Liu, Jianhua, Ao, Xiaohui, Xia, Huanxiong, Huang, Sihan, Zhu, Lijian, Li, Xiaoqiang, Du, Changlin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 06.10.2024
MDPI
Subjects:
Accuracy
Algorithms
Case studies
Deformation
digital image correlation
Efficiency
Engineering
Measurement techniques
Methods
Optimization
parameter interval
reliability index
uncertainty analysis
uncertainty analysis
parameter interval
optimization
digital image correlation
reliability index
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24196460