Curvature aided Hough transform for circle detection

Yao, Zhenjie; Yi, Weidong

doi:10.1016/j.eswa.2015.12.019

Cited by 74 publications

(38 citation statements)

References 9 publications

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“…In image (b) with higher noise then image (a) before addition of salt & pepper noise, the algorithm could recognize the circle correctly by only reducing gradient threshold. In assessing the computational complexity of the algorithm and comparing it with that of the standard SCHT, the counting of voting applications on each algorithm for 50 images of 256×256 was used and then calculated the mean of this criterion as a computational complexity criterion, based on idea proposed in [16,[20][21][22][23]26]. The corresponding results of Table 1 show that in all cases the SBCHT is faster than SCHT.…”

Section: Resultsmentioning

confidence: 99%

“…For example, these restricted circulars Hough transforms have a low accuracy and speed especially in noise-bearing environments. Although these methods decreases the computation complexity, it has low efficiency due to performing search randomly on noise bearing images [20][21][22][23][24].In Probabilitybased Circular Hough Transform (PCHT) methods, only a proportion of image edge points (about 5 to 15%) are used as representative points in storage increase followed by a search. Therefore PCHT methods reduce the computational complexity [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Given the lack of knowledge about the image, it is possible to find on efficient algorithm to find the location of circles in image. Using gradient direction of edge points to recognize the circle center is a basic and simple approach [20][21][22]25]. The increasing need to memory in the above algorithms comes from using very great storage space for decision making about image edge points to find circle centers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid Detection of Multi-size Circular Shapes using Gradient Information and Signature Curve

Abbasi¹

2019

IJCA

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The challenge of computational complexity reduction of Circular Hough Transforms (CHT) in detecting circular shapes in images is addressed. A new adaptive algorithm is introduced which minimizes the average accumulation space used in the voting process of CHT with the help of a variable size accumulation array. An improved method is presented which computes Signature Curve of gradient information of the image to find the radius and center of candidate circles, thus eliminating the influence of variable background intensity, especially in the noisy images. Experimental evaluations show that the proposed algorithm can significantly improve the quality of the results and considerably reduce the computational complexity and memory space.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rapid Detection of Multi-size Circular Shapes using Gradient Information and Signature Curve

Abbasi¹

2019

IJCA

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“…The next key step of this vision-based method for power cable cross-section measurement is contour detection of different layers. Circle detection methods such as improved Hough Transform methods [23][24][25], geometric feature methods [26][27][28], template matching method [29][30][31], and optimization method [32,33] are usually used to detect such circular contours. Due to the existence of global deformations and local defects in the multilayer contours of cross-section, current circle detection methods cannot meet the demands of contour detection in power cable cross-section measurement.…”

Section: Introductionmentioning

confidence: 99%

A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement

Zhang

Yin

2019

Energies

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The measurements of the diameter of different layers, the thickness of different layers and the eccentricity of insulation layer in the cross-section of power cables are important items of power cable test, which currently depend on labor-intensive manual operations. To improve efficiency, automatic measurement methods are in urgent need. In this paper, a monocular vision-based framework for automatic measurement of the diameter, thickness, and eccentricity of interest in the cross-section of power cables is proposed. The proposed framework mainly consists of three steps. In the first step, the images of cable cross-section are captured and undistorted with the camera calibration parameters. In the second step, the contours of each layer are detected in the cable cross-section image. In order to detect the complete and accurate contours of each layer, the structural edges in the cross-section image are firstly detected and divided into individual layers, then unconnected edges are connected by arc-based method, and finally contours are refined by the proposed break detection and grouping (BDG) and linear trend-based correction (LTBC) algorithm. In the third step, the monocular vision-based cross-section dimension measurement is accomplished by placing a chessboard coplanar with the power cable cross-section plane. The homography matrix mapping pixel coordinates to chessboard world coordinates is estimated, and the diameter, thickness and eccentricity of specific layers are calculated by homography matrix-based measurement method. Simulated data and actual cable data are both used to validate the proposed method. The experimental results show that diameter, minimum thickness, mean thickness and insulation eccentricity of simulated image without additive noise are measured with root mean squared error (RMSE) of 0.424, 0.103 and 0.063 mm, and 0.002, respectively, those of simulated image with additive Gaussian noise and salt and pepper noise are measured with RMSE of 0.502, 0.243 and 0.058 mm and 0.001. Diameter, minimum thickness and mean thickness of actual cable images are measured with average RMSE of 0.768, 0.308 and 0.327 mm. The measurement error of insulation eccentricity of actual cable image is comparatively large, and the measurement accuracy should be improved.

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“…Hence the place-side camera will take images of the battery lid at different positions on the conveyor belt, which results in different illumination conditions. However, the Hough transform algorithm [23,24] is sensitive to the parameters and the illumination conditions. In this paper, a novel circle fitting algorithm is proposed to recognize the sealing port on the battery lid in this paper.…”

Section: Calibrationmentioning

confidence: 99%

An Automatic Assembling System for Sealing Rings Based on Machine Vision

Gao

Xiao

et al. 2017

Journal of Sensors

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In order to grab and place the sealing rings of battery lid quickly and accurately, an automatic assembling system for sealing rings based on machine vision is developed in this paper. The whole system is composed of the light sources, cameras, industrial control units, and a 4-degree-of-freedom industrial robot. Specifically, the sealing rings are recognized and located automatically with the machine vision module. Then industrial robot is controlled for grabbing the sealing rings dynamically under the joint work of multiple control units and visual feedback. Furthermore, the coordinates of the fast-moving battery lid are tracked by the machine vision module. Finally the sealing rings are placed on the sealing ports of battery lid accurately and automatically. Experimental results demonstrate that the proposed system can grab the sealing rings and place them on the sealing port of the fast-moving battery lid successfully. More importantly, the proposed system can improve the efficiency of the battery production line obviously.

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Curvature aided Hough transform for circle detection

Cited by 74 publications

References 9 publications

Rapid Detection of Multi-size Circular Shapes using Gradient Information and Signature Curve

Rapid Detection of Multi-size Circular Shapes using Gradient Information and Signature Curve

A Monocular Vision-Based Framework for Power Cable Cross-Section Measurement

An Automatic Assembling System for Sealing Rings Based on Machine Vision

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