Statistical Fourier Descriptors for Defect Image Classification

Fabian Timm; Thomas Martinetz

doi:10.1109/ICPR.2010.1018

Statistical Fourier Descriptors for Defect Image Classification

Institute of Neuro- and Bioinformatics

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.

Original language	English
Title of host publication	Proceedings of the 20th Int. Conference on Pattern Recognition (ICPR)
Number of pages	4
Publisher	IEEE
Publication date	07.10.2010
Pages	4190-4193
ISBN (Print)	978-1-4244-7542-1
ISBN (Electronic)	978-1-4244-7541-4
DOIs	https://doi.org/10.1109/ICPR.2010.1018
Publication status	Published - 07.10.2010
Event	2010 20th International Conference on Pattern Recognition - Istanbul, Turkey Duration: 23.08.2010 → 26.08.2010

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title = "Statistical Fourier Descriptors for Defect Image Classification",

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.",

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AB - 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.

U2 - 10.1109/ICPR.2010.1018

DO - 10.1109/ICPR.2010.1018

M3 - Conference contribution

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ER -

Statistical Fourier Descriptors for Defect Image Classification

Abstract

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