Mendeley
TY  _  JOUR
ID  -  13930807160482821
TI  -  A Robust Statistical Color Edge Detection for Noisy Images
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Alibeigi Mina
AU  -  Mozafari Niloofar
AU  -  Azimifar Zohre
AU  -  Mahmoodian Mahnaz
AD  -  Shiraz
AD  -  Shiraz
AD  -  
AD  -  Shiraz
Y1  -  2015
PY  -  2015
VL  -  10
IS  -  3
SP  -  1
EP  -  10
KW  -  Edge detection
KW  -  Color
KW  -  Noisy image
KW  -  RRO test
KW  -  Regression
KW  -  Pratt’s figure of merit
KW  -  
KW  -  
DO  -  
N2  -  Edge detection is a fundamental tool that plays a significant role in image processing, and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. Therefore, edge detection is one of the well-studied areas in image processing and computer vision. However, it is clear that accurate edge map generation is more difficult when images are corrupted with noise. Moreover, most of edge detection methods have parameters which must be set manually. In recent years different approaches has been used to address these problems. Here we propose a new color edge detector based on a statistical test, which is robust to noise. Also, the parameters of this method will be set automatically based on image content. To show the effectiveness of the proposed method, four state-of-the-art edge detectors are implemented and the results are compared. Experimental results on five of the most well-known edge detection benchmarks show that the proposed method is robust to noise. The performance of our method for lower levels of noise is very comparable to the existing approaches, whose performances highly depend on their parameter tuning stage. However, for higher levels of noise, the observed results significantly highlight the superiority of the proposed method over the existing edge detection methods, both quantitatively and qualitatively.
UR  - rimag.ir/en/Article/14871
L1  - rimag.ir/en/Article/Download/14871
ER  -  
