Comparison of image edge detection methods for intruder detection in a phase-sensitive OTDR system

2021 ◽  
Author(s):  
Pavel Zaviska ◽  
Petr Dejdar ◽  
Petr Munster
Keyword(s):  
Edge Detection ◽  
Detection Methods ◽  
Image Edge Detection ◽  
Intruder Detection ◽  
Phase Sensitive ◽  
Image Edge

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

Image Edge Detection Based on Ant Colony Optimization Algorithm

2016 ◽  
Vol 8 (1) ◽  
pp. 1-12
Author(s):  
Yin Huan
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Detection Methods ◽  
Matlab Simulation ◽  
Ant Colony Optimization Algorithm ◽  
Edge Image ◽  
Noise Interference ◽  
Image Edge Detection ◽  
Image Edge

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique. The article applies the ACO to the image edge detection, get edge image edge according to different neighborhood access policy through MATLAB simulation, and use the best neighborhood strategy to get detection. Compared with the traditional edge detection methods, the algorithm can effectively suppress the noise interference, retain most of the effective information of the image.

scholarly journals Sensitive Ant Algorithm for Edge Detection in Medical Images

2021 ◽  
Vol 11 (23) ◽  
pp. 11303
Author(s):  
Cristina Ticala ◽  
Camelia-M. Pintea ◽  
Oliviu Matei
Keyword(s):  
Edge Detection ◽  
Medical Images ◽  
Medical Diagnostics ◽  
Detection Methods ◽  
X Rays ◽  
Image Edge Detection ◽  
Canny Edge ◽  
Image Edge ◽  
The Stability ◽  
Artificial Pheromone

Nowadays, reliable medical diagnostics from computed tomography (CT) and X-rays can be obtained by using a large number of image edge detection methods. One technique with a high potential to improve the edge detection of images is ant colony optimization (ACO). In order to increase both the quality and the stability of image edge detection, a vector called pheromone sensitivity level, PSL, was used within ACO. Each ant in the algorithm has one assigned element from PSL, representing the ant’s sensibility to the artificial pheromone. A matrix of artificial pheromone with the edge information of the image is built during the process. Demi-contractions in terms of the mathematical admissible perturbation are also used in order to obtain feasible results. In order to enhance the edge results, post-processing with the DeNoise convolutional neural network (DnCNN) was performed. When compared with Canny edge detection and similar techniques, the sensitive ACO model was found to obtain overall better results for the tested medical images; it outperformed the Canny edge detector by 37.76%.

An Effective Image Edge Detection Algorithm – Fuzzy Box-Counting

2014 ◽  
Vol 543-547 ◽  
pp. 2711-2715
Author(s):  
Li Ma ◽  
Li Shang ◽  
Long Zhang ◽  
Wei Shi Shao
Keyword(s):  
Edge Detection ◽  
Fuzzy Theory ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Box Counting ◽  
Image Edge Detection ◽  
Box Counting Dimension ◽  
Edge Points ◽  
Image Edge

Edge detection plays an important role in computer vision and image processing. Fractal and Fuzzy theory show significant effect in the edge detection and have attracted much attention. Compared with traditional edge detection methods, this paper proposes a Fuzzy Box-counting Dimension Method (FBDM). This algorithm introduces the pre-judging mechanism to improve the speed of image segmentation, and the self-adaptive dimension threshold and the voting mechanism under multi-windows to improve the accuracy of the determination of edge points. Finally, closest principle is used to clear edge and reduce noise. Experimental results show FBDM can improve the precision of image edge detection effectively without pretreatment, and it has a very superior de-noising performance.

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