A multi-BRIEF-descriptor stereo matching algorithm for binocular visual sensing of fillet welds with indistinct features

Wang, Tao; Wang, Zhijiang; Cao, Yue; Wang, Yuchen; Hu, Shengsun

doi:10.1016/j.jmapro.2021.04.031

Cited by 15 publications

(7 citation statements)

References 14 publications

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“…At the same time, for lines where the local arrangement of edge points has shifted, their weights are also considered in the calculation. The formula for clustering centroid calculation based on voting number weighting is shown in Equation (11).…”

Section: Clustering Centroid Calculation and Corner Coordinate Calcul...mentioning

confidence: 99%

“…However, these corner detection algorithms primarily focus on detecting all corners in the image and do not directly provide the coordinates of specific corner locations. Wang et al 11 utilized binocular vision for weld seam recognition and localization. The blurred surface of the weld joint posed challenges in the binocular vision matching process.…”

Section: Introductionmentioning

confidence: 99%

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Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Wang,

Wan,

Zhao

et al. 2023

Engineering Reports

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In the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.

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Section: Clustering Centroid Calculation and Corner Coordinate Calcul...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Wang,

Wan,

Zhao

et al. 2023

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…Local matching algorithms rely on local texture features. They have high efficiencies, as with the older local matching algorithm, BT [BT99], and census‐based stereo matching [ZLZ21], BRIEF‐based algorithm [WWC*21] and NLCA [Yan12] algorithms. The census algorithm takes the relationship between the centre point and the surrounding pixels to generate a binary code stream for matching.…”

Section: Related Workmentioning

confidence: 99%

“…Three-dimensional (3D) reconstruction methods make use of three approaches: time of flight, stereo and structured light [Wan20]. Sensors include Kinect [GLP*21], binocular cameras, multi-view cameras [HDG19] and Lidar [LIG20].…”

Section: Introductionmentioning

confidence: 99%

A Stereo Matching Algorithm for High‐Precision Guidance in a Weakly Textured Industrial Robot Environment Dominated by Planar Facets

Wei

Ling-jiang

2022

Computer Graphics Forum

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Although many algorithms perform very well on certain datasets, existing stereo matching algorithms still fail to obtain ideal disparity images with high precision in practical robotic applications with weak or untextured objects. This greatly limits the application of binocular vision for robotic arm guidance. Traditional stereo matching algorithms suffer from disparity loss, dilation and other problems, and deep learning algorithms have weakly generalization ability, making high‐accuracy results impossible with non‐training images. We propose an algorithm that uses segments and edges as matching units. We find the mapping relationship between two‐dimensional images and three‐dimensional scenes using segments. The algorithm obtains highly accurate results in industrial robotic applications with mainly planar facets. We combine it with a deep learning algorithm to obtain very good high‐accuracy results in both general scenes and applications of industrial robots. The algorithm effectively improves the non‐linear optimization ability of traditional algorithms and generalization ability of deep learning, and provides an effective method for the binocular vision guidance of industrial robotic scenes. We used the algorithm to guide the robot arm for threading with a success rate of 70%.

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“…Local matching algorithms rely on local texture features. They have high efficiencies, as with the older local matching algorithm, block matching [7], and census‐based stereo matching [8], BRIEF‐based algorithm [9], and A Non‐Local Cost Aggregation Method for Stereo Matching (NLCA) [10] algorithms. The census algorithm takes the relationship between the centre point and the surrounding pixels to generate a binary code stream for matching.…”

Section: Related Workmentioning

confidence: 99%

A binocular reconstruction based on perspective projection constraints and its application on robot eye‐hand coordination

Wei

Ling-jiang

2022

IET Computer Vision

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Stereo matching algorithms have been developed for many years but basically focus only on the implementation of existing datasets and are rarely applied to real scenarios, such as industrial robot scenarios. Traditional stereo matching algorithms have a high error rate, and deep learning algorithms are difficult to obtain good results in real scenarios because of their weak generalisation ability and difficult access to training data. In order to use stereo matching algorithms for industrial robot guidance, it is better to design a new traditional algorithm with low time complexity for the characteristics of industrial robot scenarios dominated by planar facets. This paper proposes a new matching method based on subrows of pixels, instead of individual pixels, in order to improve robustness of matching and reduce running time. First, the pixel strings from the same row of the left and right images are divided into several colour-identical or colour-gradient segments. Then, the colour and length of the two left and right pixel segment are used as clues to determine a matching relation and obtain the matching type. Then, all match types can be determined according to non-crossing mapping. Each match type can reason backward to the corresponding spatial state of the stimulus source so that the disparity of pixels in pixel segments representing the spatial state can be calculated. This new matching method makes full use of the stimulus homology constraints and projective geometric constraints of row-aligned images. The method can obtain good results in industrial robot scenarios and be applied for industrial robot guidance.

show abstract

A multi-BRIEF-descriptor stereo matching algorithm for binocular visual sensing of fillet welds with indistinct features

Cited by 15 publications

References 14 publications

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

A Stereo Matching Algorithm for High‐Precision Guidance in a Weakly Textured Industrial Robot Environment Dominated by Planar Facets

A binocular reconstruction based on perspective projection constraints and its application on robot eye‐hand coordination

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