Study of Image Edge Detection Techniques

2012 ◽  
Vol 505 ◽  
pp. 393-396 ◽  
Author(s):  
Wen Zhong Yan ◽  
Da Zhi Deng
Keyword(s):  
Edge Detection ◽  
Operator Technique ◽  
Iterative Thresholding ◽  
Detection Techniques ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Noise Impact ◽  
Iterative Thresholding Algorithm ◽  
Image Edge ◽  
Thresholding Algorithm

Edges characterize boundaries. Edge detection is a problem of fundamental importance in image processing. The key of edge detection for image is to detect more edge details, reduce the noise impact to the largest degree. In this paper the comparative analysis of various image edge detection techniques is presented. In order to evaluate these techniques, they are used to detect the edge of chromosome image. Firstly, the iterative thresholding algorithm and morphologic erode algorithm together are applied to enhance both the edges of the chromosomes and the contrast of the image. Then, Sobel operator technique, Roberts technique, Prewitt technique and Canny technique are used respectively to detect the edges of the chromosomes in the image.

Design of an image edge detection filter using the Sobel operator

10.1109/4.996 ◽  
1988 ◽  
Vol 23 (2) ◽  
pp. 358-367 ◽  
Author(s):  
N. Kanopoulos ◽  
N. Vasanthavada ◽  
R.L. Baker
Keyword(s):  
Edge Detection ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Detection Filter

Comparison and Evaluation of Edge Detection Segmentation Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1069-1072
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basic Problem ◽  
Mean Square ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Image Edge

Edge detection is the basic problem in the field of image processing. Various image edge detection techniques are introduced. Using various edge detection techniques different images are analyzed and compared by MATLAB7.0. In order to evaluate the effect of edge segmentation, the root mean square error is used. The experimental results show that no an edge detection technique works well for all types of images.

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.

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