DocumentCode
2700830
Title
An adaptive texture and shape based defect classification
Author
Iivarinen, Jukka ; Visa, Ari
Author_Institution
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland
Volume
1
fYear
1998
fDate
16-20 Aug 1998
Firstpage
117
Abstract
In this paper classification of surface defects is considered. The classification system consists of several classifiers whose outputs are combined in order to produce the final classification. The self-organizing maps (SOMs) are used as classifiers. Each SOM is taught unsupervised with examples of defects. Classification is based on the internal structure and the shape characteristics of defects. Texture features from the co-occurrence matrix and the gray level histogram are used to describe the internal structure. The set of simple shape descriptors is used for shape characterization The results of experiments with base paper defects are encouraging
Keywords
flaw detection; image classification; image texture; paper industry; quality control; self-organising feature maps; shape measurement; SOM; adaptive defect classification; base paper defects; co-occurrence matrix; gray level histogram; internal structure; self-organizing maps; shape characteristics; shape-based defect classification; surface defects; texture features; texture-based defect classification; unsupervised learning; Electrical capacitance tomography; Feature extraction; Image segmentation; Information science; Information technology; Laboratories; Neural networks; Organizing; Shape; Target recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location
Brisbane, Qld.
ISSN
1051-4651
Print_ISBN
0-8186-8512-3
Type
conf
DOI
10.1109/ICPR.1998.711094
Filename
711094
Link To Document