Improved Newton–Raphson digital image correlation method for full-field displacement and strain calculation

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

doi:10.1364/ao.49.006472

Cited by 43 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2010, Cofaru et al [42] improved the accuracy of DIC technique in measuring the displacement and the strain fields of a structural component subjected to low and high spatial frequency variations. In their work, they proposed an adaptive spatial regularization in which the details of neighbouring movements were analysed and processed, so that the movements of selected subsets were accurately estimated.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In practice, this consists in the optimization of a similarity criterion that penalizes pixel differences between the two images, considering a pre-defined displacement model. This can be done either locally [4][5][6][7][8][9][10][11][12][13], using rectangular image regions, also called subsets, or globally [14][15][16] across the entire image. Strain data is subsequently obtained either directly from the displacement field components or through the differentiation of the displacement field.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…The second advantage is that the robust estimation principle can be introduced into virtually all existing DIC methods as a way of enforcing local motion consistency and dealing with displacement outliers. The proposed method uses a Newton-Raphson optimization approach [24,25] to obtain the displacements relating to material deformation and robust spatial regularization to adaptively increase spatial displacement consistency [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pixel-level robust digital image correlation

Cofaru¹,

Philips²,

Paepegem³

2013

Opt. Express

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…In addition to methods for patterning objects for the purpose of DIC measurements, methods for characterizing the resulting patterns [9,10,11] and for choosing the analysis parameters in function of the characteristics of the pattern [12,13] or in function of the strain field [14] have been proposed. However, questions remain about the reliability of DIC measurements, in terms of spatial resolution, accuracy, precision, sensitivity, and robustness of the measurement.…”

Section: Introductionmentioning

confidence: 99%

Optimized Patterns for Digital Image Correlation

Bossuyt

2012

Conference Proceedings of the Society for Experimental Mechanics Series

View full text Add to dashboard Cite

This work presents theoretical background on a novel class of strain sensor patterns. A combination of morphological image processing and Fourier analysis is used to characterize gray-scale images, according to specific criteria, and to synthesize patterns that score particularly well on these criteria. The criteria are designed to evaluate, with a single digital image of a pattern, the suitability of a series of images of that pattern for full-field displacement measurements by digital image correlation (DIC). Firstly, morphological operations are used to flag large featureless areas and to remove from consideration features too small to be resolved. Secondly, the autocorrelation peak sharpness radius en the autocorrelation margin are introduced to quantify the sensitivity and robustness, respectively, expected when using these images in DIC algorithms. For simple patterns these characteristics vary in direct proportion to each other, but it is shown how to synthesize a range of patterns with wide autocorrelation margins even though the autocorrelation peaks are sharp. Such patterns are exceptionally well-suited for DIC measurements.

show abstract

Improved Newton–Raphson digital image correlation method for full-field displacement and strain calculation

Cited by 43 publications

References 21 publications

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

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

Pixel-level robust digital image correlation

Optimized Patterns for Digital Image Correlation

Contact Info

Product

Resources

About