An efficient approach for stereo matching of planar objects in stereo-digital image correlation

Shao, Xinxing; Chen, Zhenning; Dai, Xianzhong; He, Xin

doi:10.1088/1361-6501/aa7a6e

Cited by 7 publications

(4 citation statements)

References 23 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stereo matching [37][38][39][40][41] has traditionally been and continues to be one of the most extensive research topics in computer vision. A stereo algorithm generally consists of four steps: cost computation, cost aggregation, disparity optimization, and refinement.…”

Section: Stereo Matchingmentioning

confidence: 99%

A spatial minimum spanning tree filter

Jin

Zhao

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

It is well-known that the minimum spanning tree (MST) is widely used in image segment, edge-preserving filtering, and stereo matching. However, the non-local (NL) filter based on the MST generally results in overly smooth images, since it ignores spatial affinity. In this paper, we propose a new spatial minimum spanning tree filter (SMSTF) to improve the performance of the NL filter by designing a spatial MST to avoid over-smoothing problems, by introducing recursive techniques to implement the filtering process. The SMSTF has the advantages that: (1) the kernel of our filter considers spatial affinity and similarity of intensity; (2) The size of the filter kernel is the entire image domain; (3) the complexity of the SMSTF is linear to the number of image pixels. For these reasons, our filter achieves excellent edge-preserving results. Extensive experiments demonstrate the versatility of the proposed method in a variety of image processing and computer vision tasks, including edge-preserving smoothing, stylization, colorization, and stereo matching.

show abstract

Section: Stereo Matchingmentioning

confidence: 99%

A spatial minimum spanning tree filter

Jin

Zhao

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Images obtained by two cameras in different poses are related by a single homography when the object is perfectly planar . For the specimen shown in Figure , different homographies are required to describe the base plane, the plateau, the faces of the triangular prism, etc.…”

Section: Feature‐assisted Stereo Correlationmentioning

confidence: 99%

“…Stereo digital image correlation (stereo DIC) is a non‐contact, full‐field deformation measurement technique based on stereo vision. This technique is widely used in mechanics due to its ease of use, versatility, and ability to produce detailed displacement and strain maps over entire regions of interest . In stereo DIC, a calibrated stereo rig is used to simultaneously capture one or more pairs of images of a deforming body at various stages of deformation, and the images of each stereo pair are correlated with each other ( stereo correlation ) to obtain the disparity between them.…”

Section: Introductionmentioning

confidence: 99%

Feature‐assisted stereo correlation

Thiruselvam

Subramanian

2019

Strain

View full text Add to dashboard Cite

Stereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high-quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature-based matching techniques to address this gap.Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.

show abstract

“…Binocular stereo matching that has been widely used in some challenging projects such as 3D object reconstruction [1], virtual reality [2], and robot navigation [3], and is one of the hotspot issues in computer vision. Many interesting stereo matching methods have been reported in [4][5][6][7][8][9][10][11], and can be classified into two categories according to the classical taxonomy given by Scharstein and colleagues [12]: local methods, and global methods. Local methods [4,5], where the disparity of each pixel is estimated by the color or intensity values of pixels within its neighboring support window, usually have high computing efficiency when finding matching pixels, but cannot work well for the pixels in ambiguous regions such as textureless or occluded regions.…”

Section: Introductionmentioning

confidence: 99%

A long baseline global stereo matching based upon short baseline estimation

Zhao

et al. 2018

Meas. Sci. Technol.

View full text Add to dashboard Cite

In global stereo vision, balancing the matching efficiency and computing accuracy seems to be impossible because they contradict each other. In the case of a long baseline, this contradiction becomes more prominent. In order to solve this difficult problem, this paper proposes a novel idea to improve both the efficiency and accuracy in global stereo matching for a long baseline. In this way, the reference images located between the long baseline image pairs are firstly chosen to form the new image pairs with short baselines. The relationship between the disparities of pixels in the image pairs with different baselines is revealed by considering the quantized error so that the disparity search range under the long baseline can be reduced by guidance of the short baseline to gain matching efficiency. Then, the novel idea is integrated into the graph cuts (GCs) to form a multi-step GC algorithm based on the short baseline estimation, by which the disparity map under the long baseline can be calculated iteratively on the basis of the previous matching. Furthermore, the image information from the pixels that are non-occluded under the short baseline but are occluded for the long baseline can be employed to improve the matching accuracy. Although the time complexity of the proposed method depends on the locations of the chosen reference images, it is usually much lower for a long baseline stereo matching than when using the traditional GC algorithm. Finally, the validity of the proposed method is examined by experiments based on benchmark datasets. The results show that the proposed method is superior to the traditional GC method in terms of efficiency and accuracy, and thus it is suitable for long baseline stereo matching.

show abstract

An efficient approach for stereo matching of planar objects in stereo-digital image correlation

Cited by 7 publications

References 23 publications

A spatial minimum spanning tree filter

A spatial minimum spanning tree filter

Feature‐assisted stereo correlation

A long baseline global stereo matching based upon short baseline estimation

Contact Info

Product

Resources

About