Algorithm of locating the sphere center imaging point based on novel edge model and Zernike moments for vision measurement

Li, Yunhui; Huo, Ju; Yang, Ming

doi:10.1080/09500340.2018.1515377

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…We also compared the F-Measure results of various algorithms and the proposed algorithm as per the precision and recall information from Eq. (17). The edge detection method is more effective when its F-Measure is higher [51].…”

Section: A Actual Image Edge Detectionmentioning

confidence: 99%

“…The average greyscale 1 of part A and the average greyscale 2 of part B are calculated to obtain a new separation threshold 2 = 1 + 2 The values of T1 and T2 are compared and the resolution is set to T0. If | 1 − 2 | < 0 , T2 is the optimal separation threshold Th; otherwise, T1 is replaced with T2 and Steps (2) and (3) are repeated until the condition | 1 − 2 | < 0 is met.Finally, all pixels are updated and the edge image is obtained according to Eq (17)…”

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confidence: 99%

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Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

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Existing edge detection algorithms suffer from inefficient edge localization, noise sensitivity, and/or relatively poor automatic detection capability. Contemporary edge detection algorithms can be improved by targeting these problems to help bolster their performance. Grey system theory can be used to resolve the small data and poor information issues in the local information of uncertain systems. An automatic edge detection algorithm was developed in this study based on a grey prediction model to remedy these problems. Noise characteristics in grey images are used to deploy a noise-filtering algorithm based on local features. A mask with twenty-four edge direction information points (345°) was established based on edge line texture features. By compressing the amplitude of the sequence, the randomly oscillated grey prediction sequence can be converted into a smooth, new sequence. The discrete grey model (1,1) (DGM(1,1)) was established based on this new grey prediction sequence to obtain the grey prediction maximum value. A grey prediction image with enhanced edges was obtained by replacing the pixel value in the original image with the maximum grey prediction value. A grey prediction subtraction image with edges separated from non-edge points was also obtained by subtracting the original image from the grey prediction image. The optimal separation threshold in the grey prediction subtraction image can be determined via the global adaptive threshold selection method. The neighborhood search method was then deployed to remove stray points and burrs from the image after the target was separated from the background, creating the final edge image. Experiments were performed on a computer-simulated phantom to find that both the subjective visual effects and objective evaluation criteria are better under the proposed method than several other competitive methods. The proposed edge detection algorithm shows excellent edge detection ability and is highly robust to noise, though the grey prediction model needs further improvement to optimize the run time.

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Section: A Actual Image Edge Detectionmentioning

confidence: 99%

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confidence: 99%

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

et al. 2020

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“…This photogrammetric method was used to evaluate the out-of-plane displacements of the east wall at the ground floor ( Figure 1). During the test, the east wall experienced limited damage compared with the rest of the structure, mainly consisting of cracking at the top and bottom of the experimental tests since it provides better accuracies than traditional methods (e.g., the Canny edge detector) [37][38][39]. Additionally, the proposed approach includes the latest advances in camera calibration, based on the self-calibration model [40], as well as the latest strategies for the processing clusters of points such as principal component analysis [41].…”

Section: Photogrammetric Approachmentioning

confidence: 99%

Photogrammetric Solution for Analysis of Out-Of-Plane Movements of a Masonry Structure in a Large-Scale Laboratory Experiment

et al. 2019

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This paper proposes a photogrammetric procedure able to determine out-of-plane movements experienced by a masonry structure subjected to a quasi-static cyclic test. The method tracks the movement of circular targets by means of a coarse-to-fine strategy. These targets were captured by means of a photogrammetric network, made up of four cameras optimized following the precepts of a zero-, first-, and second-order design. The centroid of each circular target was accurately detected for each image using the Hough transform, a sub-pixel edge detector based on the partial area effect, and a non-linear square optimization strategy. The three-dimensional (3D) coordinates of these targets were then computed through a photogrammetric bundle adjustment considering a self-calibration model of the camera. To validate the photogrammetric method, measurements were carried out in parallel to an ongoing test on a full-scale two-story unreinforced masonry structure (5.4 × 5.2 × 5.4-m) monitored with more than 200 contact sensors. The results provided by the contact sensors during one of the load phases were compared with those obtained by the proposed approach. According to this accuracy assessment, the method was able to determine the out-of-plane displacement during the quasi-static cyclic test with a sub-pixel accuracy of 0.58. evaluated, and, thus, requires the use of registration strategies (with introduce an additional error source) to place them into a common coordinate system [1]. In addition to this, its measurement principle-based on the emission and reception of a laser beam-within the necessity of having a dense point cloud, requires the investment of several minutes to capture the whole scene. This issue restricts the use of this technology to those cases in which the structure is completely static [3,9,13,18,19].Another potential approach for the analysis of changes and deformation in structures that does not share this restriction is photogrammetry [4,[6][7][8][10][11][12]14,15,17,[20][21][22]. This approach, in contrast to laser scanning, is relatively low-cost as it is possible to obtain reliable results by means of common digital single-lens reflex (DSLR) cameras [6,15,16,21,22]. In addition to this, its flexibility makes it possible to evaluate the deformation suffered by different engineering solutions, from small scales (e.g., mechanical tests) [16,[21][22][23]] to large structures [7,14,15,20]. This property of the ability to capture several images per second makes it possible to use this technology in dynamic scenarios (e.g., shaking table tests) [7,14,17,20,[24][25][26][27]. Within this discipline, there are three strategies that are commonly applied in order to evaluate the changes that occur during a scenario: (i) digital image correlation method (DIC) [10][11][12]16]; (ii) structure from motion approach using change detection algorithms (i.e., cloud-to-cloud comparison algorithms) [15,[28][29][30]; and (iii) point-tracking approaches [14,20,23,25,26,31]. While the first two methods are able to produce f...

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“…Circles possess desirable morphological characteristics, such as clear descriptions, measurable radii, and normal vectors, and normal vectors, making them ideal for visual morphological detection. Additionally, circles demonstrate strong resistance to noise and image blur, making them a common choice for landmarks preparation and navigation [25], [26] . To enhance the accuracy of moment-based image borders extraction, Tabatabai et al [27] proposed an algorithm for sub-pixel edge localization utilizing the first three gray moments, This algorithm provides a closed-form edge localization solution without requiring interpolation or iteration.…”

Section: Introductionmentioning

confidence: 99%

Cross-Layer Feature Fusion and Decentration Aberration Correction of Circular Points for Automated Guided Vehicle Terminal Positioning

Zhang,

Yang,

Cao

et al. 2023

IEEE Photonics J.

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To address the visual detection and positioning challenge of the Automated Guided Vehicle (AGV) terminal, this study proposed a high-precision target recognition and positioning strategy based on cross-layer feature fusion and eccentricity error correction. In the remote coarse positioning stage, small number of receiver domains underwent continuous multi-layer convolution, which broadened the receptive field of a single element in the feature map of a small circular target. Next, a cross-layer connection feature pyramid was constructed, and the deconvolution module was utilized to enlarge the feature map and fuse it with the shallow features, focusing more on the fine-grained image recognition and enhancing the applicability of circular marker detection. In the near-end fine positioning stage, the factors impacting the deviation between the projection point of the circular feature center and the fitting center were analyzed. Based on this analysis, the deviation of the iterative fitting center was corrected to approximate the real center projection sub-pixel coordinates. The experimental results demonstrated that the proposed method achieved dynamic positioning accuracy of better than 2.0 mm, static positioning accuracy of better than 1.5 mm, and a false recognition rate of less than 1.33% in the range of 3 m from AGV to the tray. Consequently, this method has significant application potential in enabling rapid and stable terminal vision positioning.

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Algorithm of locating the sphere center imaging point based on novel edge model and Zernike moments for vision measurement

Cited by 8 publications

References 18 publications

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Adaptive Edge Detection Algorithm Based on Improved Grey Prediction Model

Photogrammetric Solution for Analysis of Out-Of-Plane Movements of a Masonry Structure in a Large-Scale Laboratory Experiment

Cross-Layer Feature Fusion and Decentration Aberration Correction of Circular Points for Automated Guided Vehicle Terminal Positioning

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