Applications of Algebraic Moments for Corner and Edge Detection for a Locally Angular Model

Abramenko, A. A.; Karkishchenko, A. N.

doi:10.1134/s1054661819010024

Cited by 2 publications

(3 citation statements)

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“…The results show that this method can refine the coordinates of the edge points quickly with sub-pixel precision, so as to improve the detection accuracy. 11 In order to solve the problem that the workpiece surface is usually easily covered by the interference area, researchers such as Li proposed a rough machining edge detection method based on the overall nested edge detection model. The precision of measuring the outer diameter of workpiece is up to 0.02 mm, which shows that this method can meet the requirement of fast semi-finishing inspection.…”

Section: Related Workmentioning

confidence: 99%

“…The specific implementation steps are as follows. First, use the two-dimensional Gaussian template and the original image convolution to eliminate noise, and then use the reciprocal operator to obtain the gradient size, as shown in Equation (11).…”

Section: Sub-pixel Precision Edge Extraction For Dual-column Metalmentioning

confidence: 99%

“…In order to explore methods for sub‐pixel corner and edge detection in images, scholars and experts such as Abramenko AA proposed an algebraic moment based on the luminance function describing halftone images. The results show that this method can refine the coordinates of the edge points quickly with sub‐pixel precision, so as to improve the detection accuracy 11 . In order to solve the problem that the workpiece surface is usually easily covered by the interference area, researchers such as Li proposed a rough machining edge detection method based on the overall nested edge detection model.…”

Section: Related Workmentioning

confidence: 99%

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Application of subpixel edge detection in quality control of double‐column metal parts

Xie

Chen

2023

Adv Control Appl

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At present, most of the parts of mechanical equipment are made of metal, so the accurate detection and extraction of important characteristic parameters of metal parts is the key to determine the quality of mechanical equipment. In order to accurately extract and measure the feature size of metal parts, this research first carried out image preprocessing. Threshold segmentation is performed, and the target traits are extracted from the image to obtain the ROI. Finally, the preprocessed image is extracted from the image by the sub‐pixel edge extraction technology to extract the feature size of the circle to be measured. The difference between the measured value of the characteristic dimension of the spring bearing seat and the gasket and the real value measured by the research method is within the range of 5 and 4 μm, respectively, and both of them meet the requirements of the characteristic dimension accuracy. When the feature size of the two is repeatedly measured, the variation range of the detection results of the spring bearing seat and the gasket is within 9 and 7 μm respectively, and the detection value is much smaller than the feature size tolerance. The detection accuracy of the method can reach 95.394%. The score was 96.029 and the AUC value was 0.93, both higher than other methods. The results show that the use of sub‐pixels on metal parts The edge extraction method is extremely accurate. The research method can effectively improve the detection accuracy of the feature size of metal parts, which is of great significance to the development of the entire metal parts processing industry.

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Section: Related Workmentioning

confidence: 99%

Section: Sub-pixel Precision Edge Extraction For Dual-column Metalmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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