DocumentCode :
2513842
Title :
Statistical Fourier Descriptors for Defect Image Classification
Author :
Timm, Fabian ; Martinetz, Thomas
Author_Institution :
Inst. for Neuro-& Bioinf., Univ. of Lubeck, Lübeck, Germany
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
4190
Lastpage :
4193
Abstract :
In many industrial applications, Fourier descriptors are commonly used when the description of the object shape is an important characteristic of the image. However, these descriptors are limited to single objects. We propose a general Fourier-based approach, called statistical Fourier descriptor (SFD), which computes shape statistics in grey level images. The SFD is computationally efficient and can be used for defect image classification. In a first example, we deployed the SFD to the inspection of welding seams with promising results.
Keywords :
Fourier transforms; image classification; statistical analysis; defect image classification; grey level images; object shape; shape statistics; statistical Fourier descriptors; welding seams; Correlation; Discrete Fourier transforms; Feature extraction; Inspection; Shape; Support vector machines; Welding; Defect Image Classification; Feature Extraction; Fourier Descriptors; Machine Vision;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.1018
Filename :
5597748
Link To Document :
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