Quantum Image Edge Detection Based on Four Directional Sobel Operator

Author(s):  
Rajib Chetia ◽  
S. M. B. Boruah ◽  
S. Roy ◽  
P. P Sahu
10.1109/4.996 ◽  
1988 ◽  
Vol 23 (2) ◽  
pp. 358-367 ◽  
Author(s):  
N. Kanopoulos ◽  
N. Vasanthavada ◽  
R.L. Baker

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana

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.  


2011 ◽  
Vol 55-57 ◽  
pp. 467-471 ◽  
Author(s):  
Ke Fei Wang

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.


2014 ◽  
Vol 971-973 ◽  
pp. 1529-1532 ◽  
Author(s):  
Li Hua Sun ◽  
En Liang Zhao ◽  
Long Ma ◽  
Li Zheng

The edge detection which comes from the classical Sobel operator in the image is based on the horizontal gradient and vertical gradient direction. On the basis of the template from two directions the method which is to detect the edges of eight directions is discussed in this paper. The image edge detection based on multiple directions can improve the accuracy of edge detection. The numerical experimental results show that the proposed method can get the smooth, continuous, multiple directions edges, and can reduce the loss of the information. The method proposed in this paper is to detect the edge of the image, which will result in higher accuracy. Especially for more complex texture image, the effect of edge detection is more obvious, and the pixel width is closer to a single pixel width. It is an effective method for edge detection.


2012 ◽  
Vol 505 ◽  
pp. 393-396 ◽  
Author(s):  
Wen Zhong Yan ◽  
Da Zhi Deng

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.


Author(s):  
G. Ravivarma ◽  
K. Gavaskar ◽  
D. Malathi ◽  
K.G. Asha ◽  
B. Ashok ◽  
...  

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
R. Chetia ◽  
S. M. B. Boruah ◽  
P. P. Sahu

2019 ◽  
Vol 58 (9) ◽  
pp. 2823-2833 ◽  
Author(s):  
Suzhen Yuan ◽  
Salvador E. Venegas-Andraca ◽  
Yuchan Wang ◽  
Yuan Luo ◽  
Xuefeng Mao

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