Title :
Statistical Fourier Descriptors for Defect Image Classification
Author :
Timm, Fabian ; Martinetz, Thomas
Author_Institution :
Inst. for Neuro-& Bioinf., Univ. of Lubeck, Lübeck, Germany
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;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.1018