Stereo-rectification and homography-transform-based stereo matching methods for stereo digital image correlation

Zhong, Fuqiang; Quan, Chenggen

doi:10.1016/j.measurement.2020.108635

Cited by 22 publications

(7 citation statements)

References 33 publications

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“…Also, the efficacy of the approach was validated by the source images possessing the real characteristics which were produced by a ray tracer. In [34], the scholars focused on CP discovery which might be present between right and left images and hence, proposed two approaches, namely homography-transform-based stereo matching (HTSM) and stereorectification-based stereo matching (SRSM). There were many applications of HTSM despite HTSM applicability to the planar objects.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid model of alternating least squares and root polynomial technique for color correction

et al. 2023

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Colour correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Various researchers have already proposed many approaches. However, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein Alternate Least Square (ALS), and Root Polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to make it look realistic. The proposed model used the Amsterdam Library of Object Images (ALOI) to achieve this objective, which contains a picture of single objects captured under various illumination angles and colors. After this, a hybrid ALS+RP color correction technique is implemented on the dataset image that fixes its color for the reference image. The target image is then converted into three color models i.e., LAB, LUV, and RGB, into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, Median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB to prove its supremacy over other models.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid model of alternating least squares and root polynomial technique for color correction

et al. 2023

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“…The matching process of the pixel in an image with its similar pixel in another image along an epipolar line occurs using a matching cost function. By matching the pixels of both images, the calculation of depth applying the distance between two cameras and the pixel distance between matched pixels will be possible [ 55 , 56 ]. Reflective and highly transparent zones accompanied by smooth areas are the major challenges for stereo matching algorithms.…”

Section: Depth Estimation (De)mentioning

confidence: 99%

Monocular Depth Estimation Using Deep Learning: A Review

Masoumian

Rashwan

Cristiano

et al. 2022

Sensors

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In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.

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“…2.1. Full-field displacement measurement of a rotating structure using stereo DIC Stereo DIC is an optical non-contact measurement technique which can obtain full-field displacement and strain measurements of the objects' surfaces when they deform [26][27][28]. To accurately measure deformations in the 3D direction, the general setup requires a stereo system using two or more cameras.…”

Section: Measurement Approachmentioning

confidence: 99%

Strain accuracy enhancement of stereo digital image correlation for object deformation with large rotations

Wu¹,

Zhao²,

Liu³

et al. 2022

Meas. Sci. Technol.

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We propose a full-field stereo digital image correlation (DIC) strain measurement method in order to overcome the poor accuracy while measuring the deformation under large rotations. Such drawback comes from the missing of considering rotation movements of the deformed objects when calculating their strain values. To address that, we first used a DIC matching algorithm combined with rotated subset and feature point detection to obtain displacement fields. By employing a singular value decomposition (SVD) method, we then can calculate rotation matrices of the strain windows before and after deformations. Finally, in order to eliminate the strain errors caused by rotation, we introduced the rotation matrices into the classical pointwise least square (PLS) DIC strain calculation method. Both numerical simulations and experiments are performed, and the accuracy and effectiveness of the proposed method are confirmed by the experimental results.

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Stereo-rectification and homography-transform-based stereo matching methods for stereo digital image correlation

Cited by 22 publications

References 33 publications

Hybrid model of alternating least squares and root polynomial technique for color correction

Hybrid model of alternating least squares and root polynomial technique for color correction

Monocular Depth Estimation Using Deep Learning: A Review

Strain accuracy enhancement of stereo digital image correlation for object deformation with large rotations

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