A new edge detection algorithm based on Canny idea

Feng, Yingke; Zhang, Jinmin; Wang, Siming

doi:10.1063/1.5005213

Cited by 19 publications

(17 citation statements)

References 4 publications

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“…An edge aware denoising method was recommended by [ 31 ] in order to get the goal of conserving the material of the original picture when utilizing the features of the noisy picture. However, this approach has a generalized issue such as weak antinoise capacity and false edge [ 32 ]. Another denoising scheme was developed in [ 33 ] that was based on different wavelet transforms against the threshold value.…”

Section: Literature Reviewmentioning

confidence: 99%

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Ṣiddiqi

Alhwaiti

2022

Journal of Healthcare Engineering

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Image denoising methods are important in order to diminish various kinds of noises, which are presented either capturing the image or distorted during image transmission. Signal-to-noise ratio (SNR) is one of the main barriers which avoids the theoretical observations to be accomplished in practice. In this study, we have utilized various kinds of filtering operators against three various noises, which are the signal-to-noise ratio comparison against the phantom image in spatial and frequency domain. In frequency domain, the average filter is used to smooth the image and frequency domain, and Gaussian low-pass filter is applied with empirically determined cutoff frequency. This work has six major parts such as applying average filter, determining the SNR of region of interest, transforming the image in frequency domain by discrete Fourier transform, obtaining the rectangular Gaussian low-pass filter along with a cutoff frequency, multiplying them, and carrying out the inverse Fourier transform. These steps are repeated accordingly until the resulting image SNR is equal to or greater than the spatial domain SNR. In order to achieve the goal of this study, we have analyzed the proposed approach against some of complex phantom images. The performances of these filters are compared against signal-to-noise ratio.

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Section: Literature Reviewmentioning

confidence: 99%

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Ṣiddiqi

Alhwaiti

2022

Journal of Healthcare Engineering

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“…Among those Gaussian based method algorithms, Canny is the most well-known even though this method is not perfect as it also has shortcomings just like in every other method. Yingke feng [8] stated that the disadvantage of Canny algorithm are a poor self-adaptability threshold, and more sensitive to noise. Because of those reasons, Yingke Feng et al [8] modified Canny algorithm into new edge detection method.…”

Section: Pendahuluanmentioning

confidence: 99%

“…Yingke feng [8] stated that the disadvantage of Canny algorithm are a poor self-adaptability threshold, and more sensitive to noise. Because of those reasons, Yingke Feng et al [8] modified Canny algorithm into new edge detection method. Cao et al [9] stated that the Canny operator is widely used to detect edges in images.…”

Section: Pendahuluanmentioning

confidence: 99%

Morphological Edge Detection Algorithms on the Noisy Car Image Database

Dina

2020

JEPIN

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Development of edge detector using mathematical morphology can provide remarkably more precise edge detection. This system mainly focuses on edge detection of cars. This paper uses car database for the early detection in automatic traffic law enforcement. The methodology for accurate edge detector includes grayscale image conversion, median filter, element structures formation, mathematical morphology, synthetic weighted and segmentation using Otsu method. The morphological operation that was used in this study is basic operation such as dilation and erosion. The use of morphological operation was to find the limits and the image terminals by selecting different size and extensions converted to images of grey levels and the results were compared to determine the most suitable and better methods. In this study, 371 car databases and 100 objects from the publicly available Berkeley and Greek car databases were used as data. This methodology is proved highly accurate 0.7-1.58% higher than Canny edge detector. The accuracy of the results from Mathematical Morphlogy Edge Detection were 83.23%; 83.27%; 82.66%; 79.78% while the results from Canny Edge Detection were 81.90%; 82.08%; 81.08%; 79.71%. In comparison with the results from Canny edge detector. It shows that mathematical morphology renders better overall performance. It was also tested with salt and pepper noises and still shows better results. Z-test was used for comparing the means of two populations while F-test was used to test if two population variances are equal. Both tests were done because in this study two populations were used as main datasets. The effectiveness and robustness make this mathematical morphology method a suitable tool to be integrated into complete pre-screening systems for the early detection in automatic traffic law enforcement.

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“…The second defect of traditional CEDA is the performance of noise removal. [12] pro-poses using the median filter to replace Gaussian Filter on the filtering step and Frei-Chen algorithm and Ostu-Algorithm will be used to calculate gradient amplitude. Ac-cording [14], Bilateral Filter will replace the Gaussian Filter on CEDA which provide better performance on noise removal.…”

Section: A Previous Workmentioning

confidence: 99%

“…The first defect is slow processing and cause the CEDA cannot apply on the real time [2]. The second defect was the weak anti-noise ability which mean not function well on the noise removal [12]. So that are a lot of improvement and enhancement done by many researchers.…”

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confidence: 99%

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

Soon*¹,

Abas²,

Rahmalan³

2019

IJITEE

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Canny Edge Detection Algorithm was very popular on the computer vision area which used to preserve the edges of the image. Due to the defect of the Canny Edge Detection Algorithm like no efficiency on noise removal, some improvement on the Canny Edge Detection Algorithm was done by the researchers. On this paper, a new enhanced Canny Edge Detection Algorithm will be propose which replaces the Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter. The replace of Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter is to improve the performance of Canny Edge Detection Algorithm on noise removal. A comparison between Canny Edge Detection Algorithm proposed by this paper, Canny Edge Detection Algorithm proposed by (Ilkin, Tafralı, &Sahin, 2017) and traditional Canny Edge Detection Algorithm will be done. The comparison will done by using eight images with different type and size which corrupted by noise. The performance of three algorithms will be determined by using the Peak Signal to Noise Ratio (henceforth, PSNR) value which uses as a quantitative measure. From the result, the Canny Edge Detection proposed by this paper will provide a better performance on noise removal and which will give a better impact on preserve the edges of the images corrupted by noise.

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A new edge detection algorithm based on Canny idea

Cited by 19 publications

References 4 publications

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Signal-to-Noise Ratio Comparison of Several Filters against Phantom Image

Morphological Edge Detection Algorithms on the Noisy Car Image Database

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

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