DocumentCode :
598873
Title :
Feature extraction and automatic recognition of wear particles in ferro-graphic image based on Riesz transform
Author :
He, Xiaoqin ; Li, Jinjun ; Li, Xiaoyan
Author_Institution :
Department of Computer Science, Chongqing Electric Power College, China
fYear :
2012
fDate :
16-18 Oct. 2012
Firstpage :
439
Lastpage :
444
Abstract :
Wear Particle Analysis, as an effective method in mechanical equipment condition monitoring, has been widely and successfully applied to many fields, i.e. weapon equipment, maintenance and daily management. To avoid the influences such as complexity of tribo-system, scrambling and randomicity of the wear particle, an image analysis technique based on Riesz transforms has been proposed to extract features efficiently and recognize wear particles automatically. Local magnitude and local orientation is firstly estimated using Riesz transform. Then feature parameters have been extracted using the generalization of the traditional Canny edge detection procedure and the mean shift based color image segmentation. Finally, the principal component analysis (PCA) has been employed to automatically recognize wear particles.
Keywords :
formatting; insert; style; styling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2012 5th International Congress on
Conference_Location :
Chongqing, Sichuan, China
Print_ISBN :
978-1-4673-0965-3
Type :
conf
DOI :
10.1109/CISP.2012.6469661
Filename :
6469661
Link To Document :
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