scholarly journals Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Gustavo Asumu Mboro Nchama ◽  
Leandro Daniel Lau Alfonso ◽  
Roberto Rodríguez Morales ◽  
Ezekiel Nnamere Aneke
Keyword(s):  
Edge Detection ◽  
Training System ◽  
Detection Methods ◽  
Order Derivative ◽  
Experimental Comparison ◽  
Mathematical Methods ◽  
Image Brightness ◽  
Medical Diagnostic ◽  
Image Edge ◽  
Fractional Differential

Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

Edge Detection of Inner Crack Defects Based on Improved Sobel Operator and Clustering Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 467-471 ◽  
Author(s):  
Ke Fei Wang
Keyword(s):  
Edge Detection ◽  
Clustering Algorithm ◽  
Detection Methods ◽  
Experimental Result ◽  
Edge Image ◽  
Positioning Accuracy ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Sobel Edge Detection

The classical Sobel edge detection operator has the shortcomings of low edge positioning accuracy and coarse edge, image edge detection based on improved Sobel operator and clustering algorithm was proposed. Four Sobel-like edge operators are used to improve the edge positioning accuracy and clustering algorithm are used to edge thinning. The experimental result demonstrates that the effect of the edge detection is greatly improved comparing with the traditional edge detection methods.

scholarly journals Comparison of image edge detection methods on potholes road images

2020 ◽  
Vol 1613 ◽  
pp. 012067
Author(s):  
Tutuk Indriyani ◽  
Imam Utoyo ◽  
Riries Rulaningtyas
Keyword(s):  
Edge Detection ◽  
Detection Methods ◽  
Image Edge Detection ◽  
Image Edge

A Study of Image Weak-Edge Detection Combination of BP Neural Networks

2011 ◽  
Vol 225-226 ◽  
pp. 21-25
Author(s):  
Jing Bing Yang ◽  
Hui Ding ◽  
Shu Dong Zhang
Keyword(s):  
Neural Networks ◽  
Edge Detection ◽  
Training Sample ◽  
Detection Methods ◽  
Bp Neural Networks ◽  
Edge Points ◽  
Image Edge ◽  
Basic Characteristics ◽  
Edge Detection Method ◽  
Weak Edge

This paper proposes an image weak-edge detection method based on the combination of edge features and BP neural networks. Through analyzing the basic characteristics of the image edge points, we construct 8 groups 3-D feature vectors as the training sample set, combining with the learning function based on gradient descent momentum and the Levenberg-Marquardt training function, to train the BP neural network, further complete the image edge detection. Finally, compared with the traditional edge detection methods, the experimental results show that this method can detect the weak-edge and corner-edge much better.

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