Image Edge Detection Based on FCM and Improved Canny Operator in NSST Domain

Author(s):  
Shaohai Hu ◽  
He Zhang
2013 ◽  
Vol 347-350 ◽  
pp. 3541-3545 ◽  
Author(s):  
Dan Dan Zhang ◽  
Shuang Zhao

The traditional Canny edge detection algorithm is analyzed in this paper. To overcome the difficulty of threshold selecting in Canny algorithm, an improved method based on Otsu algorithm and mathematical morphology is proposed to choose the threshold adaptively and simultaneously. This method applies the improved Canny operator and the image morphology separately to image edge detection, and then performs image fusion of the two results using the wavelet fusion technology to obtain the final edge-image. Simulation results show that the proposed algorithm has better anti-noise ability and effectively enhances the accuracy of image edge detection.


2012 ◽  
Vol 151 ◽  
pp. 653-656
Author(s):  
Zhan Chun Ma ◽  
Xiao Mei Ning

CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.


2014 ◽  
Vol 37 (3) ◽  
pp. 238-250 ◽  
Author(s):  
Yinfei Zheng ◽  
Yali Zhou ◽  
Hao Zhou ◽  
Xiaohong Gong

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1749
Author(s):  
Phusit Kanchanatripop ◽  
Dafang Zhang

In order to improve the accuracy of image edge detection, this paper studies the adaptive image edge detection technology based on discrete algorithm and classical Canny operator. First, the traditional sub-pixel edge detection method is illustrated based on the related literature research. Then, Canny operator is used for detection, the edge model of the quadric curve is established using discrete data, and the adaptive image edge parameters are obtained using one-dimensional gray moment. Experimental results show that the accuracy of feature detection is 99%, which can be applied to the practice of image edge detection to a certain extent.


Author(s):  
Mingbin Luan ◽  
Gongwen Xu ◽  
Qinghua Xu ◽  
Xiaoyan Wang ◽  
Hongluan Wang

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