Paper Info
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.
Year
DOI
Venue
2010
10.1109/ICPR.2010.1018
ICPR
Keywords
Field
DocType
important characteristic,general fourier-based approach,statistical fourier descriptors,object shape,industrial application,statistical fourier descriptor,defect image classification,shape statistic,fourier descriptors,grey level image,promising result,fourier transforms,feature extraction,support vector machines,statistical analysis,correlation,welding,inspection,image classification,machine vision,shape
Computer vision,Machine vision,Pattern recognition,Computer science,Support vector machine,Fourier transform,Feature extraction,Artificial intelligence,Fourier descriptor,Contextual image classification,Statistical analysis
Conference
Citations 
PageRank 
References 
2
0.37
6
Authors
2
Name
Order
Citations
PageRank
Fabian Timm1111.99
Thomas Martinetz21462231.48