NDLI: Ceramic Surface Image Feature Extracting and Classifying Algorithms Based on Artificial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Yang Liu-Qing
Copyright Year	2014
Description	Author affiliation: Jingdezhen Ceramic Inst., Jingdezhen, China (Yang Liu-Qing)
Abstract	The brittleness of ceramic materials causes surface defect of ceramic products in the phase of manufacturing and processing. The defect on surface and subsurface will obviously decrease the fatigue life of ceramics. To improve the quality of end products, we have studied the mechanism of formation and expansion of surface defects in this paper. An improved image processing and recognition algorithm based on ANN is proposed to perform shape features extraction for the separated surface defects. By the results in experiments it is verified that: The previous 7 regularized defect boundary Fourier descriptors can better difference the defects with 10 features according to the features like wear defect area and discontinuous divided objects. We also establish and train two ANN classifiers separately corresponding to the features, whose accuracy is 97.5% in histogram feature classifier and 90.3% in shape feature classifier.
Starting Page	267
Ending Page	271
File Size	196705
Page Count	5
File Format	PDF
e-ISBN	9781479942619
DOI	10.1109/ISDEA.2014.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Shape Artificial neural networks Ceramic surface Feature extraction Ceramics Fourtier descriptor ANN classifier Surface cracks Surface treatment Boundary
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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