Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection

Amoako-Yirenkyi, Peter; Appati, Justice Kwame; Dontwi, I. K.

doi:10.4236/ojapps.2016.67048

Cited by 17 publications

(6 citation statements)

References 11 publications

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“…Many dramatic changes in this area have been made by taking the fractional differential concepts into account. In recent years, the use of fractional differential operators to improve image quality, image texture enhancement, image noise reduction, and image edge analysis have yielded stunning results [4][5][6][7][8][9][10][11][12]. One of the most important formulas for expanding of fractional differential operators in image processing is to use the following general form:…”

Section: A Short Review Of Some Of the Well-known Methodsmentioning

confidence: 99%

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

2020

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Computers and electronics play an enormous role in today's society, impacting everything from communication and medicine to science. The development of computer-related technologies has led to the emergence of many new important interdisciplinary fields, including the field of image processing. Image processing tries to find new ways to access and extract information from digital images or videos. Due to this great importance, many researchers have tried to utilize new and powerful tools introduced in pure and applied mathematics to develop new concepts in imaging science. One of these valuable research areas is the contents of fractional differential calculus. In recent years, extensive applications to the new fractional operators have been employed in real-world problems. This article attempts to address a practical aspect of this era of research in the edge detecting of an image. For this purpose, two general structures are first proposed for making new fractional masks. Then the components in these two structures are evaluated using the fractional integral Atangana-Baleanu operator. The performance and effectiveness of these proposed designs are illustrated by several numerical simulations. A comparison of the results with the results of several well-known masks in the literature indicates that the results presented in this article are much more accurate and efficient. This is the main achievement of this article. These fractional masks are all novel and have been introduced for the first time in this contribution. Moreover, in terms of computational cost, the proposed fractional masks require almost the same amount of computations as the existing conventional ones. By observing the numerical simulations presented in the paper, it is easily understood that with proper adjustment for the fractional-order parameter, the accuracy of the obtained results can be significantly improved. Each of the new suggested structures in this article can be regarded as a valid and effective alternative for the well-known existing kernels in identifying the edges of an image.

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Section: A Short Review Of Some Of the Well-known Methodsmentioning

confidence: 99%

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

2020

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“…In the same year, Amoako-Yirenkyi et al [22] discussed border detection by creating a window to detect borders and describing the use of structural similarity index values and comparing them. This research proposed a comparison of the median filter, Gaussian filter, and B-spline filter from the same original image.…”

Section: ) Gaussian Filtermentioning

confidence: 99%

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Palananda

Kimpan

2021

IEEE Access

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In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity. INDEX TERMSImage processing, turbidity level, moving average, coconut oil, Computer vision.

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“…There are many methods to obtain contour curve, among which the typical ones are Roberts [ 19 , 20 ], Sobel [ 20 , 21 ], Prewitt [ 20 , 22 ], LoG (Laplacian of Gaussian) and Canny [ 23 , 24 ] operators. The LoG operator can always detect more details than the Canny operator in the same scale, while the Canny operator is not easy to be disturbed by noise.…”

Section: Boundary Extraction and Feature Extraction Of Rope Arrangmentioning

confidence: 99%

Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision

Shi

Tan

Xue³

2021

Sensors

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Due to the importance of safety detection of the drum’s rope arrangement in the ultra-deep mine hoist and the current situation whereby the speed, accuracy and robustness of rope routing detection are not up to the requirements, a novel machine-vision-detection method based on the projection of the drum’s edge is designed in this paper. (1) The appropriate position of the point source corresponding to different reels is standardized to obtain better projection images. (2) The corresponding image processing and edge curve detection algorithm are designed according to the characteristics of rope arrangement projection. (3) The Gaussian filtering algorithm is improved to adapt to the situation that the curve contains wavelet peak noise when extracting the eigenvalues of the edge curve. (4) The DBSCAN (density-based spatial clustering of applications with noise) method is used to solve the unsupervised classification problem of eigenvalues of rope arrangement, and the distance threshold is calculated according to the characteristics of this kind of data. Finally, we can judge whether there is a rope arranging fault just through one frame and output the location and number of the fault. The accuracy and robustness of the method are verified both in the laboratory and the ultra-deep mine simulation experimental platform. In addition, the detection speed can reach 300 fps under the premise of stable detection.

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Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection

Cited by 17 publications

References 11 publications

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

Some new edge detecting techniques based on fractional derivatives with non-local and non-singular kernels

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision

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