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Proceedings Paper

Classification method for defect images based on association and clustering
Author(s): Iivari Kunttu; Leena Lepisto; Juhani Rauhamaa; Ari J.E. Visa
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Paper Abstract

Clustering of the images stored in a large database is one of the basic tasks in image database mining. In this paper we present a clustering method for an industrial imaging application. This application is a defect detection system that is used in paper industry. The system produces gray level images from the defects that occur at the paper surface and it stores them into an image database. These defects are caused by different reasons, and it is important to associate the defect causes with different types of defect images. In the clustering procedure presented in this paper, the image database is indexed using certain distinguishing features extracted from the database images. The clustering is made using an algorithm, which is based on the k-nearest neighbor classifier. Using this algorithm, arbitrarily shaped clusters can be formed in the feature space. The algorithm is applied to the database images in hierarchical way, and therefore it is possible to use several different feature spaces in the clustering procedure. The images in the obtained clusters are associated with the real defect causes in the industrial process. The experimental results show that the clusters agree well with the traditional classification of the defects.

Paper Details

Date Published: 21 March 2003
PDF: 9 pages
Proc. SPIE 5098, Data Mining and Knowledge Discovery: Theory, Tools, and Technology V, (21 March 2003); doi: 10.1117/12.486007
Show Author Affiliations
Iivari Kunttu, Tampere Univ. of Technology (Finland)
Leena Lepisto, Tampere Univ. of Technology (Finland)
Juhani Rauhamaa, ABB Industry Oy (Finland)
Ari J.E. Visa, Tampere Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 5098:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology V
Belur V. Dasarathy, Editor(s)

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