SIFT-aided path-independent digital image correlation accelerated by parallel computing

Yang, Junrong; Huang, Jianwen; Jiang, Zhenyu; Dong, Sujun; Tang, Liqun; Liu, Yiping; Liu, Zejia; Zhou, Licheng

doi:10.1016/j.optlaseng.2019.105964

Cited by 60 publications

(8 citation statements)

References 25 publications

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“…Te extracted corresponding SIFT feature points are fltered and then used to calculate an initial afne translation matrix mapping from ImgL to ImgR as shown in Figure 3(b). Te obtained afne matrix is set as the initial estimation of the shape and shift parameters of the improved LSM algorithm, relying on the existing relationship between the afne warp model of the SIFT and the homography matrix of the LSM [41]. Te homography matrix shares the same function as the afne translation.…”

Section: Improved Coarse-to-fine Matching Algorithmmentioning

confidence: 99%

Wireless Binocular Stereovision Measurement System Based on Improved Coarse-to-Fine Matching Algorithm

Wang

Xiao

Gong

et al. 2023

Structural Control and Health Monitoring

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To cope with the challenges some commonly used displacement sensors may face in structural health monitoring, we proposed a wireless binocular stereovision measurement system based on the improved coarse-to-fine matching algorithm. The vision measurement system can perceive multipoint three-dimensional displacement and consists of two cameras, one remote control unit, and a solar panel. The improved coarse-to-fine matching algorithm only requires a flat surface with some painted textures instead of designed markers with refined patterns (circle, square, and chessboard). Omitting the textures keeps a balance between measurement accuracy and the burden of the target installation in the actual field of structural health monitoring. The proposed coarse-to-fine matching algorithm utilizes scale-invariant feature transform (SIFT) points to generate a robust initial guess and combines improved least squares matching (LSM) algorithm for advanced corresponding point matching. A precalculated look-up table for interpolation and parallel computation is used for higher matching precision and computational efficiency. Several experiments were conducted to present the accuracy and feasibility of the proposed metering system. A simulation test, an indoor shaking table test with several commonly used displacement sensors, and an outdoor experiment consider the natural condition. Experimental results verified that the proposed matching methodology could achieve 0.2-pixel matching accuracy, 0.1 mm measuring precision in a laboratory, and millimeter-level precision in real conditions.

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Section: Improved Coarse-to-fine Matching Algorithmmentioning

confidence: 99%

Wireless Binocular Stereovision Measurement System Based on Improved Coarse-to-Fine Matching Algorithm

Wang

Xiao

Gong

et al. 2023

Structural Control and Health Monitoring

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“…Therefore, let's investigate various image processing functions that capture color information or a local descriptor. The evaluating feature extraction methods include RGB, Scale Invariant Feature Transformation [6], Accelerated Reliable Features [7], Oriented FAST and Rotated BRIEF [8], Histogram Oriented Gradients [9]. The features are selected because they contain color, local features, or object detector information from the image data.…”

Section: Introductionmentioning

confidence: 99%

Assessment of plant disease detection by deep learning

Alpyssov

Uzakkyzy

Ayazbaev

et al. 2023

EEJET

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Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures

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“…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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SIFT-aided path-independent digital image correlation accelerated by parallel computing

Cited by 60 publications

References 25 publications

Wireless Binocular Stereovision Measurement System Based on Improved Coarse-to-Fine Matching Algorithm

Wireless Binocular Stereovision Measurement System Based on Improved Coarse-to-Fine Matching Algorithm

Assessment of plant disease detection by deep learning

Real‐time illumination adjustment for video deflectometers

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