Digital image correlation (DIC) based stereo matching method for binocular structured light system (BSLS)

Digital image correlation (DIC) is a powerful experimental technique for measuring full-field displacement and strain. The basic idea of the method is to compare images of an object decorated with a speckle pattern before and after deformation in order to compute the displacement and strain fields. Local Subset DIC and finite element-based Global DIC are two widely used image matching methods; however there are some drawbacks to these methods. In Local Subset DIC, the computed displacement field may not satisfy compatibility, and the deformation gradient may be noisy, especially when the subset size is small. Global DIC incorporates displacement compatibility, but can be computationally expensive. In this thesis, we propose a new method, the augmented-Lagrangian digital image correlation (ALDIC), that combines the advantages of both the local (fast and in parallel) and global (compatible) methods. We demonstrate that ALDIC has higher accuracy and behaves more robustly compared to both Local Subset DIC and Global DIC. DIC requires a large number of high resolution images, which imposes significant needs on data storage and transmission. We combined DIC algorithms with image compression techniques and show that it is possible to obtain accurate displace- ment and strain fields with only 5 % of the original image size. We studied two compression techniques – discrete cosine transform (DCT) and wavelet transform, and three DIC algorithms – Local Subset DIC, Global DIC and our newly proposed augmented Lagrangian DIC (ALDIC). We found the Local Subset DIC leads to the largest errors and ALDIC to the smallest when compressed images are used. We also found wavelet-based image compression introduces less error compared to DCT image compression. To further speed up and improve the accuracy of DIC algorithms, especially in the study of complex heterogeneous strain fields at various length scales, we apply an adaptive finite element mesh to DIC methods. We develop a new h-adaptive technique and apply it to ALDIC. We show that this adaptive mesh ALDIC algorithm significantly decreases computation time with no loss (and some gain) in accuracy.

For disparity information acquisition tasks, phase-shift profilometry can achieve high disparity accuracy, but it is a stereo matching technique based on phase unwrapping. The phase unwrapping depends on the fringe pattern of multifrequency. Recent work also shows that deep learning can obtain disparity from a stereo pair of images, but it is difficult to obtain high accuracy. To tackle these problems, we propose a stereo matching method of obtaining high-accuracy disparity using a combination of the fringe pattern stereo matching network and the phase-shift method. First, the coarse disparity is obtained by using the fringe pattern stereo matching network, and then the wrapped phase map obtained by the 3 steps phase-shift method is used to optimize the coarse disparity to obtain a high-accuracy disparity map. This method rectifies the left and right fringe patterns in advance and eliminates the influence of the calibration parameters without loss of generality. It also avoids the challenge of phase unwrapping compared with phase-shift profilometry. We prove that the sinusoidal fringe pattern stereo matching can obtain a better coarse disparity effect than the stereo matching of the texture image, especially in the textureless area. Experiments show that high-precision disparity can be obtained with only three frames of high-frequency fringe patterns.

High-speed and high-accuracy fringe projection profilometry without phase unwrapping

Three-dimensional displacement measurement based on DIC-assisted polarization fringe projection

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

Full-field strain mapping for characterization of structure-related variation in corneal biomechanical properties using digital image correlation (DIC) technology

The digital image correlation (DIC) method is an advanced technical method to measure material strain by comparing several captured figures during deformation. DIC provides full-field displacements and full-field strains in recorded images. In fact, there is much software using the DIC algorithm. However, a commercial DIC system requires a high initial investment. In this study, the DIC algorithm was built based on the open-source of Matlab software, temporarily called NCORR DIC. A DIC system was built including a computer system, extensometer, tensile testing machine, high-resolution camera, NCORR DIC software and applied for AL5052 sheet metal test sample. After obtaining the images from the tensile test, the entire image will be included in the NCORR DIC software for processing. To check the accuracy of the DIC system, the results of the stress-strain curve of NCORR software were compared with the standard test method using extensometer. The obtained results measured from NCORR DIC software were imported into a finite element method (FEM) software to check the accuracy of this method. Moreover, the hardening model (Voce, Swift, Kim-Tuan) provides empirical evidence and prerequisite conditions for the development of new fracture criteria to describe behavior fractures.

Fringe projection profilometry has been widely applied to three-dimensional measurement. However, the nonlinear effect of the projector leads to errors in the unwrapped phase in the phase-shift method. In this paper, we propose a direct gamma estimation method. Theoretical derivation shows that the gamma factor is related to the three-step phase-shifted fringe patterns and the ideal unwrapped phase. The unwrapped phase after Gaussian low-pass filtering is taken as the initial estimate of the ideal unwrapped phase. We correct those abnormal values after calculating the gamma factor. The corrected gamma factor is used to inverse gamma correct the captured fringe patterns, and then the gamma-corrected unwrapped phase is obtained by phase demodulation and phase untangling from the inverse gamma corrected fringe patterns. Then we perform iterative operations on the gamma factor and ideal unwrapped phase. We consider the gamma-corrected unwrapped phase as the new ideal unwrapped phase, recalculate and update the new gamma factor until the gamma factor converges to a stable, desired state. Our method only needs to project and collect three frames of fringe pattern, which meets the high-speed measurements requirement. The experimental result of the face mask demonstrates that our method can effectively reduce the nonlinear phase errors.

Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.

Stereo matching is widely used in three-dimensional (3D) reconstruction, stereo machine vision and digital image correlation. The aim of stereo matching process is to solve the well-known correspondence problem, which tries to match points or features from one image with the same points or features in another image from the same 3D scene. There are two basic ways, correlation-based and feature-based, are used to find the correspondences between two images. The correlation-based way is to determine if one location in one image looks/seems like another in another image, and the feature-based way to find if a subset of features in one image is similar in the another image. In stereo matching, a simple algorithm is to compare small patches between two rectified images by correlation search. For the pair images acquired from two cameras inevitably exists some rotation transformation, the algorithm first runs a preprocessing step to rectify the images with the epipolar rectification to simplify the problem of finding matching points between images. The epipolar rectification is to determine a transformation of each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axes. It will lead the loss of gray information of images. The effect is dependent on the amount of angle. When the angle is big enough, the correlation search may yield error results because of retrograded correlation effect. In order to solve the problem, the paper presents an improved stereo matching algorithm with differential evolution to solve the correspondence problem. Our method doesn’t need to runs the preprocessing step to rectify the images with the epipolar rectification. It uses a differential evolution algorithm to minimize the correlation function which contains the angle information after acquiring the epipoar geometry constraint of two image pairs. Then it utilizes a flood-fill algorithm to search correspondence sub-region in the area around the epipolar line. The flood-fill algorithm can overcome the problem of the traditional row-column scanning search method, which will encounter boundary barrier where exists concave polygons or cavities. The Experimental results show that the proposed method can be easily implemented in stereo matching without loss of information of image features with large rotation angle transformation. In the paper, we will introduce the stereo matching principle and its algorithms, including the differential evolution algorithm for finding the correspondences with large rotation transformation between stereo image pairs and the flood-fill traversal strategy for matching large area with complex concave polygons or cavities. In the end of the paper, some experimental results will be given to illustrate the method effectiveness. Keywords: digital image correlation, stereo matching algorithm, epipolar geometry, flood fill algorithm, differential evolution, rotation angle

Digital image correlation (DIC) is an optical technique for contactless displacement and strain measurement recently applied to biological tissues. We characterized a DIC system for small biological specimens based on a high speed camera, a stereomicroscope, and an original image correlation algorithm. Optical features have been evaluated calculating the optical distortion and the modulation transfer function. The accuracy of the DIC algorithm used here has been assessed employing an elastic specimen subjected to known amount of strains, and has been compared with accuracies obtained in the same way using two other algorithms. For strain values up to 25 %, that is within the typical strain range for biological tissues, and for magnifications up to 6.3×, our DIC algorithm was able to compute strains with a relative error lower than 1 %. The accuracies obtained with the elastic specimen were then confirmed performing DIC analysis on mouse skin samples subjected to controlled strains.

The digital speckle pattern (DSP) is one of the key factors in digital image correlation (DIC), and its quality directly determines the accuracy of the computations. In practical DIC calculations, factors such as non-uniform illumination and imaging system limitations result in nonlinear shifts in image gray-scale values and a reduction in contrast, thereby causing alterations in the actual edges. Therefore, it is essential to determine the optimal production parameters for DSPs based on edge conditions and to clarify the impact of these production parameters on the DSP quality. In this study, we generated DSPs with different edge types and combined sub-pixel and orthogonal experiments to obtain the optimal parameters under various edge conditions, thereby elucidating the influence of production parameters on the DSP quality. Furthermore, deformation tests were conducted using the optimal DSPs for different edge types, achieving effective measurements of up to 60% strain when using Gauss and complex edges. This study proposes a DIC measurement method based on DSP edges. Before fabricating the DSP, the actual DSP edge is determined by capturing a single speckle, after which the DSP production parameters are selected accordingly. Using this method, the average absolute calculation error in strain measurements can be reduced by up to 41.30% under underexposure conditions. This research provides theoretical guidance for generating DSPs for deformation measurements in specialized scenarios.

Real-time motion-induced error reduction for phase-shifting profilometry with projection points tracking method

Digital image correlation (DIC) is a powerful experimental technique to determine displacement and strain fields. DIC methods usually require a large number of high resolution images, and this imposes significant needs on data storage and transmission. In this work, we combine digital image correlation with image compression techniques and show that it is possible to obtain accurate displacement and strain fields with only 5% of the original image size. We study two compression techniques – discrete cosine transform (DCT) and wavelet transform, and three DIC algorithms – Local Subset DIC, Global DIC and the recently proposed augmented Lagrangian DIC (ALDIC). We find that Local Subset DIC leads to the largest errors and ALDIC to the smallest when compressed images are used. We also find that wavelet-based image compression introduces less error compared to DCT image compression.

The movement pattern of particles on the surface of liquid marble during rupture based on the DIC algorithm