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
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;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.711094