Self-Learning System For Surface Failure Detection (CROSBI ID 543453)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Rimac-Drlje, Snježana ; Keller, Alen ; Nyarko, Emmanuel Karlo
engleski
Self-Learning System For Surface Failure Detection
In this article we present a self-learning system for automatic detection of surface failures on ceramic tiles. This system is based on the probabilistic neural network with radial basis. The discrete wavelet transform (DWT) is used as a preprocessing method with good feature extraction possibilities. With an automatic procedure for the production of input vectors for the neural networks training the presented system can adapt itself to different textures. Experimental results of the defect detection for different types of tiles show a high accuracy and applicability of the proposed procedure.
Surface failure detection; neural networks; wavelets; self-learning system
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Podaci o prilogu
2005.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of EURASIP 13th European Signal Processing Conference EUSIPCO 2005
Sankur, Bulent
Antalya: Bogazici University
975-00188-0-X
Podaci o skupu
13th European Signal Processing Conference EUSIPCO 2005
poster
04.09.2005-08.09.2005
Antalya, Turska