Concentric Circle Detection Method Based on Minimum Enveloping Circle and Ellipse Fitting

Gu, YuanTong; Lü, Yifei; Yang, Haigen; Huo, Jianlin

doi:10.1109/icsess47205.2019.9040724

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The difference between the square of the distance from the sampling point (X i , Y i ) to the fitted circle and the square of the radius of the fitted circle is Equation ( 25) [25].…”

Section: Least Square Methods For Circle Fittingmentioning

confidence: 99%

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Zheng

Yin

et al. 2021

Applied Sciences

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Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ± 100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.

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“…The difference between the square of the distance from the sampling point (X i , Y i ) to the fitted circle and the square of the radius of the fitted circle is Equation ( 25) [25].…”

Section: Least Square Methods For Circle Fittingmentioning

confidence: 99%

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Zheng

Yin

et al. 2021

Applied Sciences

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show abstract

“…However, complex industrial illumination environments, diverse product materials and the noise from the sensor itself will introduce lots of noisy points or outlier points, which brings big challenges when fitting an arc in a highly accurate manner [5]. In fact, arc fitting is highly sensitive to noise and a tiny variation in input data will cause the fitting short-arc's radius and center to fluctuate much more, which is not preferred in precise measurement [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm: fitting a spatial arc to noisy point clouds with high accuracy and reproducibility

Huang

Chen

et al. 2021

Meas. Sci. Technol.

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Fitting a spatial arc to noisy point clouds with high accuracy and reproducibility is challenging, although it is important in many applications, such as precise measurement, computerized numerical control machining and robotic path planning. In optical measuring applications, an arc-shaped object is usually first scanned as point clouds by a 3D camera or multiple charge-coupled device cameras, and arc fitting is then invoked to fit these point clouds, obtaining the measuring radius and center. The accuracy of the arc-fitting algorithm plays an important role in the arc-measuring precision. In this paper, a novel algorithm is proposed to fit a spatial arc of high accuracy and reproducibility to noisy point clouds. This method combines the repeated least trimmed squares idea with the smoothing fairness function, i.e. discrete curvature, to devise the objective function, which is solved iteratively. This algorithm can successfully filter noise and fit a highly accurate arc to noisy point clouds with high reproducibility. Seven popular arc-fitting algorithms are implemented as benchmarks and both simulated and real data scanned from physical objects are tested to validate that the proposed algorithm performs best. The proposed algorithm is efficient and can be easily implemented in industrial applications.

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Enhancements in Circle Rendering: An Improved Approach to the Midpoint Circle Drawing Algorithm

Nithya,

Idrisi

2023

Int J Netw Distrib Comput

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Rendering circles on digital platforms is essential in computer graphics. The widely adopted midpoint circle algorithm is a premier tool for this task. This article dives deep into the foundational aspects of this algorithm, highlighting its prowess in enabling accurate and effortless circle depiction on digital displays. It further contrasts its merits with alternative circle drawing techniques. Importantly, our research presents a refined technique aimed at minimizing inaccuracies inherent in the standard cartesian-based Midpoint Circle Drawing Algorithm (MPCDA). By incorporating an error-reduction strategy, we ensure greater accuracy in depicting circles. Comparative tests on diverse circle sizes and placements confirmed the efficacy of our enhanced method. When set against results from the traditional MPCDA, our approach showcases significant improvements.

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Concentric Circle Detection Method Based on Minimum Enveloping Circle and Ellipse Fitting

Cited by 6 publications

References 12 publications

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

A novel algorithm: fitting a spatial arc to noisy point clouds with high accuracy and reproducibility

Enhancements in Circle Rendering: An Improved Approach to the Midpoint Circle Drawing Algorithm

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