Improvement and implementation for Canny edge detection algorithm

Yang, Tao; Qiu, Yuehong

doi:10.1117/12.2197080

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…In this section, Canny algorithm is used to extract the edges [7] . This method can effectively detect dark target region, and is not affected by boundary flat noise, but is susceptible to high-frequency noise interference.…”

Section: Target Region Normalizationmentioning

confidence: 99%

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Research on feature extraction method of space targets image based on Hu extension moment

fan,

wang,

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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Detection, tracking, characterization and identification of space targets orbiting the Earth is one of the main tasks of the space surveillance system. Spatial target image feature extraction can find out some descriptions that reflect the essence of the target from the original image. The moment feature describes the statistical characteristics of grayscale by the moments of each order of image distribution, which can effectively reflect the shape information of the target. A Hu extension moment is proposed, and a feature extraction method for spatial target images is proposed based on this. It solves the problem of information loss when the confidence variance difference is large and the eigenvalue is negative. A linear combinatorial feature calculation method is proposed for spatial target classification, and a linear classifiable quantitative discrimination method is given. The image needs to be preprocessed. Then, the seven eigenvalues based on the extension moment are calculated, and the similarity analysis of the Hu extension moment is carried out. At last, the similarity of the sample target region matching is analyzed, and the target similarity can reach 90%. For image sets with multiple types of targets, according to the numerical distribution calculates the linear combination feature, linear classifiable quantitative discriminant results can be calculated. 48 images of four satellites in the database were selected as test data. Its recognition accuracy rate reaches 95.8%. Calculating the recognition result of the same platform for multi-sample joint recognition, and the probability of recognition attribution after multi-sample union is 100%.

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Section: Target Region Normalizationmentioning

confidence: 99%

“…In formula (7), sig(•) is the symbolic function. Based on the plotting results of Figure 5, the first extension moment and the second extension moment have a strong correlation, and the confidence variance between the two is close.…”

Section: Similarity Analysis Of Hu Extension Momentsmentioning

confidence: 99%

Research on feature extraction method of space targets image based on Hu extension moment

fan,

wang,

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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show abstract

“…Canny algorithm calculates gradient amplitude by solving finite difference in 2 x 2 neighbourhood, considering sensitivity of the method to noise, it easily detects false edge, missing real edge. The improved method uses first partial derivative within a 3 x 3 neighbourhood to solve gradient amplitude and direction [8]. Where, F � �x, y�、F � �x, y�、F ��°� x, y�、F ��°� x, y� are first partial derivatives in the direction of x, y, 45° and 135°, I�x, y� is the pixel point in 3 x 3 neighbourhood.…”

Section: Improvement Of Canny Edge Detection Algorithm 31 Improve Immentioning

confidence: 99%

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

Qin

Juan

2017

MATEC Web Conf.

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Abstract. In order to realize automatic cutting of strain gauge diaphragm, it is needed to detect and identify strain gauge edge. Though classic canny algorithm can detect the edge of strain gauge, it has such problems as low accuracy, being easily influenced by light, random noise. This paper proposes an improved canny algorithm, replacing Gauss filtering of classic canny algorithm with median filtering, improving gradient magnitude calculation method, and selecting threshold with iteration. Compared to classic canny algorithm the method can improve the ability to resist random noise while detecting strain gauge edge more accurately. Application of the method to strain gauge edge detection will lay foundation for realization of strain gauge automatic cutting.

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Edge Detection and Enhancement of Color Images Based on Bilateral Filtering Method Using K-Means Clustering Algorithm

Reddy¹,

Kumar²

2020

Advances in Intelligent Systems and Computing

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Improvement and implementation for Canny edge detection algorithm

Cited by 5 publications

References 1 publication

Research on feature extraction method of space targets image based on Hu extension moment

Research on feature extraction method of space targets image based on Hu extension moment

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

Edge Detection and Enhancement of Color Images Based on Bilateral Filtering Method Using K-Means Clustering Algorithm

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