DocumentCode :
2608348
Title :
An improved Prewitt algorithm for edge detection based on noised image
Author :
Yang, Lei ; Zhao, Dewei ; Wu, Xiaoyu ; Li, Hui ; Zhai, Jun
Author_Institution :
Digital Media Dept., Commun. Univ. of China, Beijing, China
Volume :
3
fYear :
2011
fDate :
15-17 Oct. 2011
Firstpage :
1197
Lastpage :
1200
Abstract :
In this paper, an improved Prewitt algorithm for edge detection is proposed for the reason that the traditional Prewitt edge detection algorithm is sensitive to the noise. The traditional Prewitt edge detection operator only has two templates with horizontal and vertical directions. While the edge is in a plurality of directions, so operator with eight templates of different directions is put forward and it can detect more edges. In order to improve the capability of resisting noise, this paper put forward three improvements. First of all, the mean value rather than the maximum value of the gradient magnitude of the eight directions is used as the final gradient magnitude. Secondly, OTSU automatic threshold is used to set the gradient magnitude threshold. Again, an 8-neighborhood template is proposed to remove the isolated single pixel noise. The experimental results show that the improved algorithm improves the anti noise performance greatly, and detects the edges of the random noised image effectively.
Keywords :
edge detection; image denoising; OTSU automatic threshold; Prewitt edge detection algorithm; Prewitt edge detection operator; final gradient magnitude; gradient magnitude threshold; isolated single pixel noise removal; maximum value; random noised image; Algorithm design and analysis; Educational institutions; Image edge detection; Media; Noise; Noise reduction; Signal processing algorithms; OTSU automatic threshold; Prewitt; edge detection; eight-neighborhood denoising;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
Type :
conf
DOI :
10.1109/CISP.2011.6100495
Filename :
6100495
Link To Document :
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