Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation

Li, Wenpan; Li, Youfu; Liang, Jin

doi:10.1016/j.optlaseng.2019.04.016

Cited by 19 publications

(7 citation statements)

References 42 publications

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“…There are various IVE methods such as Fast Fourier transform-based cross-correlation (FFT-CC), 8 Optical Flow 9 and feature-based IVE methods like feature-based initial guess (FB-IG), 10 path-independent initial value estimation (PI-IVE). 11 Feature-based IVE is considered to be the most robust and efficient approach in providing the initialization values for large deformation due to its invariance to translation, scale, and rotation. It uses matched feature points as a source for calculating the initial deformation parameters of the seed points by enabling a reliable path for propagating the initial guesses of the correspondences to the neighboring points.…”

Section: Introductionmentioning

confidence: 99%

“…It uses matched feature points as a source for calculating the initial deformation parameters of the seed points by enabling a reliable path for propagating the initial guesses of the correspondences to the neighboring points. 11 However, the downside of the features extracted by the feature detector and descriptor algorithms is the nonuniform distribution of features, which are randomly distributed, causing sparse and dense clusters of features, 12 leading to biased selection of target subset with calculation point. Moreover, the fewer the detected feature points at a particular region, the higher the probability of mismatched feature points.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Wang et al 13 implemented a fast and automatic initial guess estimation technique using Scale-Invariant Feature Transform (SIFT) with improved Random Sample Consensus (iRANSAC) for discarding mismatched feature points and selecting three pairs of correctly matched feature points for determining the initial guess. However, a pathindependent IVE method PI-IVE was illustrated in 11 to demonstrate the applicability of the DIC system in real-time with the proposed fast PI-IVE method, which uses DAISY descriptor and PatchMatch algorithm to identify reliable initial guesses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature-based template approach for optimizing digital image correlation on complex deformations

Prasad,

Kumar,

Kalra

et al. 2024

Health Monitoring of Structural and Biological Systems XVIII

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The initialization method in Digital Image Correlation (DIC) is essential for optimizing the correlation criteria and accurately computing the deformations of a material under load. At present, feature-based initialization techniques are widely explored for predicting the deformations of various complex circumstances, such as large deformations for soft materials, non-continuous deformations in heterogeneous materials, etc. However, due to the non-uniform distribution of the detected features, the initialization process goes through biased prediction. This bias occurs due to the sparsity of features in different regions of the sample, which can lead to inaccuracy in identifying the shape of deformation. This study addresses the issue of feature distribution and develops a featurebased template approach for providing initialization points for each subset on a finer scale. The features (interest points) are determined using KAZE feature detector and descriptor algorithm in nonlinear scale space due to its ability to determine consistent, repeatable, distinct features invariant to scale and rotation. The proposed algorithm uses bi-cubic b-spline interpolation to identify the strongest interest point at the subpixel level for each subset (of the input sample images), which works as an initial value for estimating the deformation. Further, a threshold-based incremental reference approach is developed for measuring large deformations and avoiding the cumulative errors associated with the commonly used incremental reference strategy, which is compute-intensive because of the comparison between every previous image and the subsequent images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feature-based template approach for optimizing digital image correlation on complex deformations

Prasad,

Kumar,

Kalra

et al. 2024

Health Monitoring of Structural and Biological Systems XVIII

View full text Add to dashboard Cite

show abstract

“…DIC tracks the movement of multiple points in images via an iterative subset matching algorithm to achieve displacement measurement 10,11 . With the advances in integer pixel search, 12–17 subpixel registration, 18–22 and parallel computing strategies, 16,23 the cutting edge DIC algorithm achieves characteristics of high‐accuracy and high‐efficiency in well‐controlled laboratories. However, in the open laboratory or outdoor environments, the measured target is often exposed to natural light.…”

Section: Introductionmentioning

confidence: 99%

Real‐time illumination adjustment for video deflectometers

Yang

Zhang

Wang

et al. 2022

Structural Contr & Hlth

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Summary Video deflectometers, as a real‐time, contactless optical means, show great potentials in monitoring structural deflections. However, variations of illumination conditions pose a severe challenge on accurate measurement. In this study, a strategy with global illumination adjustment has been proposed to dynamically change the exposure time of the camera to achieve an optimal intensity distribution. An illumination‐robust digital image correlation (DIC) algorithm is also proposed for accurate, real‐time video deflection measurement. The combined method improves robustness and accuracy of measurement in two ways, which includes globally dynamic intensity adjustment and modification of the grayscale arrays in subsets during the correlation process. Experiments in the laboratory environment and applications in the outdoor environment have been conducted to validate the performance of the proposed method. The results show that the proposed method is feasible and accurate in real‐time deflection measurement in the outdoor environment with variations of environmental illumination.

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“…IC-GN algorithm is not only more computationally efficient than the NR algorithm without any loss of its measurement accuracy, but it also offers better noise-proof robustness performance [21], [22]. Due to these merits, the IC-GN algorithm has been considered as a gold standard and milestone algorithm for subpixel/subvoxel registration in practical DIC/DVC applications [23], [24].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study

Zou

Pan

2020

Measurement Science Review

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In digital volume correlation (DVC), random image noise in volumetric images leads to increased systematic error and random error in the displacements measured by subvoxel registration algorithms. Previous studies in DIC have shown that adopting low-pass pre-filtering to the images prior to the correlation analysis can effectively mitigate the systematic error associated with the classical forward additive Newton-Raphson (FA-NR) algorithm. However, the effect of low-pass pre-filtering on the state-of-the-art inverse compositional Gauss-Newton (ICGN) algorithm has not been investigated so far. In this work, we focus on the effect of low-pass pre-filtering on two mainstream subvoxel registration algorithms (i.e., 3D FA-NR algorithm and 3D IC-GN algorithm) used in DVC. Basic principles and theoretical error analyses of the two algorithms are described first. Then, based on numerical experiments with precisely controlled subvoxel displacements and noise levels, the influences of image noise on the displacements measured by two subvoxel algorithms are examined. Further, the effects of low-pass pre-filtering on these two subvoxel algorithms are examined for simulated image sets with different noise levels and deformation modes. The results show that the low-pass pre-filtering can effectively suppress the systematic errors for the 3D FA-NR algorithm, which is consistent with the previously drawn conclusion in DIC. On the contrary, different form the 3D FA-NR algorithm, the 3D IC-GN algorithm itself can reduce the influence of image noise, and the effect of low-pass pre-filtering on it is not so obvious as on 3D FA-NR algorithm.

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Enhanced feature-based path-independent initial value estimation for robust point-wise digital image correlation

Cited by 19 publications

References 42 publications

Feature-based template approach for optimizing digital image correlation on complex deformations

Feature-based template approach for optimizing digital image correlation on complex deformations

Real‐time illumination adjustment for video deflectometers

The Effect of Low-pass Pre-filtering on Subvoxel Registration Algorithms in Digital Volume Correlation: A revisited study

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