A novel speckle pattern—Adaptive digital image correlation approach with robust strain calculation

Cofaru, Corneliu; Philips, Wilfried; Paepegem, Wim Van

doi:10.1016/j.optlaseng.2011.09.006

Cited by 24 publications

(13 citation statements)

References 38 publications

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“…If the multiplication factors are chosen very small, neighbouring displacement components would have to be very similar to the central one to influence it. Similar previous regularization implementations [12,20] preserved well large displacement discontinuities while at the same time smoothing minor local displacement component variations, effectively filtering out displacement calculus noises.…”

Section: Outlier Rejectionmentioning

confidence: 67%

“…Despite recent advances aimed at improving displacement and strain accuracy, DIC lacks an approach that seeks to limit the influence of the various sources of measurement errors in an unified and effective way. If some problematic areas, such as image intensity variations [18,19], image noise [9], displacement discontinuities [11,12] or measurements near the edges of specimens [10,20], individually received attention in literature, others such as the presence of occlusions or localized image artefacts due to strong reflections or shadows, were not discussed. One of the main reasons for the apparent fragmentation in DIC solutions is the fact that current DIC methods optimize quadratic similarity criteria associated with the subset intra-pixel differences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pixel-level robust digital image correlation

Cofaru¹,

Philips²,

Paepegem³

2013

Opt. Express

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Abstract:Digital Image Correlation (DIC) is a well-established noncontact optical metrology method. It employs digital image analysis to extract the full-field displacements and strains that occur in objects subjected to external stresses. Despite recent DIC progress, many problematic areas which greatly affect accuracy and that can seldomly be avoided, received very little attention. Problems posed by the presence of sharp displacement discontinuities, reflections, object borders or edges can be linked to the analysed object's properties and deformation. Other problematic areas, such as image noise, localized reflections or shadows are related more to the image acquisition process. This paper proposes a new subset-based pixel-level robust DIC method for in-plane displacement measurement which addresses all of these problems in a straightforward and unified approach, significantly improving DIC measurement accuracy compared to classic approaches. The proposed approach minimizes a robust energy functional which adaptively weighs pixel differences in the motion estimation process. The aim is to limit the negative influence of pixels that present erroneous or inconsistent motions by enforcing local motion consistency. The proposed method is compared to the classic Newton-Raphson DIC method in terms of displacement accuracy in three experiments. The first experiment is numerical and presents three combined problems: sharp displacement discontinuities, missing image information and image noise. The second experiment is a real experiment in which a plastic specimen is developing a lateral crack due to the application of uniaxial stress. The region around the crack presents both reflections that saturate the image intensity levels leading to missing image information, as well as sharp motion discontinuities due to the plastic film rupturing. The third experiment compares the proposed and classic DIC approaches with generic computer vision optical flow methods using images from the popular Middlebury optical flow evaluation dataset. Results in all experiments clearly show the proposed method's improved measurement accuracy with respect to the classic approach considering the challenging conditions. Furthermore, in image areas where the classic approach completely fails to recover motion due to severe image de-correlation, the proposed method provides reliable results.

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Section: Outlier Rejectionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Pixel-level robust digital image correlation

Cofaru¹,

Philips²,

Paepegem³

2013

Opt. Express

Self Cite

View full text Add to dashboard Cite

show abstract

“…The obtained deformation parameters are used for the neighbouring subsets in order to increase the accuracy of displacements and strains. In 2012, Cofaru et al [43] introduced an image adaptive subset algorithm which uses irregular subset sizes in the image matching process. It was required that the adaptive subset size was big enough to cover one speckle.…”

Section: Improvement Of 2d-dic Algorithms Regarding Their Measurementmentioning

confidence: 99%

A Review of Surface Deformation and Strain Measurement Using Two-Dimensional Digital Image Correlation

Khoo¹,

Karuppanan²,

Tan³

2016

Metrology and Measurement Systems

101

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Among the full-field optical measurement methods, the Digital Image Correlation (DIC) is one of the techniques which has been given particular attention. Technically, the DIC technique refers to a non-contact strain measurement method that mathematically compares the grey intensity changes of the images captured at two different states: before and after deformation. The measurement can be performed by numerically calculating the displacement of speckles which are deposited on the top of object's surface. In this paper, the Two-Dimensional Digital Image Correlation (2D-DIC) is presented and its fundamental concepts are discussed. Next, the development of the 2D-DIC algorithms in the past 33 years is reviewed systematically. The improvement of 2D-DIC algorithms is presented with respect to two distinct aspects: their computation efficiency and measurement accuracy. Furthermore, analysis of the 2D-DIC accuracy is included, followed by a review of the DIC applications for two-dimensional measurements.

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“…In 2012, Cofaru et al manifested NR method to replace rectangular subsets with irregular cells and to perform spatial motion regularization. Pan et al illustrated an incremental reliability‐guided DIC technique for large deformation measurement.…”

Section: Introductionmentioning

confidence: 99%

High‐speed, two‐dimensional digital image correlation algorithm using heterogeneous (CPU‐GPU) framework

Thiagu

Subramanian²,

Nasre

2020

Strain

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Two-dimensional digital image correlation (2D-DIC) is an experimental technique used to measure in-plane displacement of a test specimen. Real-time measurement of full-field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU-based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored.To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU-GPU) framework in combination with a pyramid-based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by ≈ 9× compared to a sequential CPU-based implementation for a pair of gray-scale images with a resolution of 588 × 2, 048 pixels. As an important milestone, feasibility to measure deformations in real time (≤1 s) is manifested in this study.

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A novel speckle pattern—Adaptive digital image correlation approach with robust strain calculation

Cited by 24 publications

References 38 publications

Pixel-level robust digital image correlation

Pixel-level robust digital image correlation

A Review of Surface Deformation and Strain Measurement Using Two-Dimensional Digital Image Correlation

High‐speed, two‐dimensional digital image correlation algorithm using heterogeneous (CPU‐GPU) framework

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