An image analysis method based on principal component analysis and its application to fabric defect detection

Abstract

The invention belongs to the field of image analysis processing, is applicable to the field of automatic detection and control of the surface quality of fabrics, and relates to a method for analyzing an image based on principal component analysis and a method applicable to detection of the defects of fabric. The method for analyzing the image based on the principal component analysis comprises the following steps of: firstly, expanding gray values in an original image sample into two groups of vectors according to rows and lines; secondly, performing template operation on the two groups of vectors, and respectively performing principal component analysis on the two groups of vectors which are subjected to template operation to obtain corresponding principal component matrixes; and finally, performing projection operation on a sample to be detected by using the two principal component matrixes, and calculating the similarity of the sample after projection and the sample before projection to analyze the characteristics of the image. The invention has the advantages that: non-uniform illumination can be eliminated without the conventional pre-processing step; calculation in a detection period is simple; the original fabric sample is respectively expanded according to the rows and the lines and then subjected to template operation, so that the longitude and latitude orientation characteristics of fabric texture can be fully utilized, the defects can be highlighted, and the random interference of the texture is restrained; and detection accuracy rate is improved.

Description

technical field [0001] The invention belongs to the field of image analysis and processing and is applied to the field of automatic detection and control of textile surface quality. The invention relates to an image analysis method based on principal component analysis and a method for detecting fabric defects. Background technique [0002] Principal component analysis (PCA) or Karhunen-Loève (KL) transformation, as an important multivariate statistical method, is widely used in the field of pattern recognition, such as face recognition and data compression, due to its excellent properties. The basic idea of ​​principal component analysis is to use linear transformation to obtain a set of new features with the same number and no correlation with each other from the original features, and the first few of these features contain the main information of the original features. [0003] In the field of image analysis, principal component analysis, as a multivariate analysis metho...

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Problems solved by technology

It is worth noting that Kumar (2003) and Sezer et al. (2004) only regarded PCA as an auxiliary method for dimensionality reduction, while Ozdemir et al. (1996) directly used PCA for flaw detection, but the method Principal component analysis needs to be performed on each sample, which involves a very large amount of calculation; secondly, this method does not consider the random interference of fabric texture, and there are large errors in the detection results

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