Automatic extraction of speckle area in digital image correlation

Hui-ran, 胡慧然 Hu; Xi-zuo, 但西佐 Dan; Qi-han, 赵琪涵 Zhao; Fangyuan, 孙方圆 Sun; Yong-hong, 王永红 Wang

doi:10.3788/co.20191206.1329

Cited by 5 publications

(3 citation statements)

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“…The original image is shown in Figure 1, and the experimental results are shown in Figures 2-5 (b-f). The error rate and PSNR of the segmentation results are shown in Tables 1 and 2, and the iteration time and the number of iterations are shown in Table 3 [40,41]. The efficiency of the algorithms was compared using the running time after convergence and the number of iterations n. A Dell OptiPlex 360 (Intel Core 4, 8 GB of memory) running a Windows 7 system with the MATLAB 2013a (MathWorks, Natick, MA, USA)programming environment comprised the evaluation platform.…”

Section: Segmentation Performance Testmentioning

confidence: 99%

“…The original image is shown in Figure 1, and the experimental results are shown in Figures 2-5 (b-f). The error rate and PSNR of the segmentation results are shown in Tables 1 and 2, and the iteration time and the number of iterations are shown in Table 3 [40,41]. Comparing the segmentation results of the five algorithms in Figures 2-5 for four images with different degrees of Gaussian noise interference, we can see that the segmentation results of the FCM_S, FLICM, and LDMREFCM algorithms still contained many noise points; the KWFLICM algorithm contained fewer noise points; while the improved algorithm has the fewest noise points.…”

Section: Segmentation Performance Testmentioning

confidence: 99%

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A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Ren

2020

Sensors

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Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

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Section: Segmentation Performance Testmentioning

confidence: 99%

“…The original image is shown in Figure 1, and the experimental results are shown in Figures 2-5 (b-f). The error rate and PSNR of the segmentation results are shown in Tables 1 and 2, and the iteration time and the number of iterations are shown in Table 3 [40,41]. Comparing the segmentation results of the five algorithms in Figures 2-5 for four images with different degrees of Gaussian noise interference, we can see that the segmentation results of the FCM_S, FLICM, and LDMREFCM algorithms still contained many noise points; the KWFLICM algorithm contained fewer noise points; while the improved algorithm has the fewest noise points.…”

Section: Segmentation Performance Testmentioning

confidence: 99%

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

Ren

2020

Sensors

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“…The remote sensing images of wheat fields, canyons and forests had multiplicative noise added with a mean value of 0 and mean squared deviations of 90, 121 and 61, respectively [50][51][52]. The numbers of clusters were 2, 2 and 3, respectively.…”

Section: Image Segmentation Test With Multiplicative Noisementioning

confidence: 99%

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Ren

2020

Sensors

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This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

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Automatic ROI recognition and parameters selection for digital image correlation in measuring structures with complex shapes

Cheng¹,

Xing²,

Zhou³

et al. 2023

Meas. Sci. Technol.

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For the digital image correlation (DIC) method, the measurement of specimens with complex shapes may encounter difficulties due to the time-consuming recognition of region of interest (ROI), and the indeterminate parameter selection caused by the non-uniform deformation. This paper proposes an automatic DIC for the measurement of structures with complex shapes. An automatic ROI segmentation is developed by combining a convolutional neural network (CNN) and image morphology, so the boundary of the specimen can be acquired accurately and efficiently. In dealing with the non-uniform deformation, a strain-related automatic selection of DIC parameters is developed, in which the sampling intervals and the subset sizes at different areas can be automatically determined. Both results of the simulated experiment and real experiment show that, by combing the two approaches with segmentation-aided digital image correlation, the proposed automatic DIC can characterize the complex deformation including the boundary of the structures effectively.

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Automatic extraction of speckle area in digital image correlation

Cited by 5 publications

References 0 publications

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Automatic ROI recognition and parameters selection for digital image correlation in measuring structures with complex shapes

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