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Content Provider | IEEE Xplore Digital Library |
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Author | Shin-Min Chao Du-Ming Tsai Wei-Chen Li Wei-Yao Chiu |
Copyright Year | 2010 |
Abstract | In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect because the intensity difference between unevenly-illuminated background and defective regions are hardly observable. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in low-contrast surface images. |
Starting Page | 4408 |
Ending Page | 4411 |
File Size | 791662 |
Page Count | 4 |
File Format | |
ISBN | 9781424475421 |
ISSN | 10514651 |
e-ISBN | 9781424475414 |
DOI | 10.1109/ICPR.2010.1071 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-08-23 |
Publisher Place | Turkey |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Anisotropic magnetoresistance Image edge detection Laplace equations Entropy Surface treatment Smoothing methods Substrates Particle swarm optimization Defect detection Surface inspection Anisotropic diffusion |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition |
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