Wavelet-Based Image Enhancement for Defect Detection in Thin Film Transistor Liquid Crystal Display Panel

Song, Youngchul; Choi, Doo−Hyun; Park, Kil−Houm

doi:10.1143/jjap.45.5069

Cited by 17 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, automated visual inspections use a single lighting (SL) method that consists of one camera and one lighting module. SL visual inspection has been applied in various industries such as textiles, [12][13][14] LCD panels, 15,16) and circuits 17) for its fast speed and diagnostic capabilities. 18) However, steel surfaces can have many uneven surface properties caused by non-uniform surfaces and scales, which are composed of oxidized substances caused during hot-process manufacturing.…”

Section: Detection Of Periodic Defects Using Dual-light Switching Ligmentioning

confidence: 99%

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

et al. 2015

View full text Add to dashboard Cite

Currently, automated inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steel making industry. In this paper, we propose a vision-based method to detect periodic defects in the surface of thick plates. To minimize the influence of a non-uniform surface property and improve the accuracy of the detection rate, a detection method based on dual-light switching lighting (DLSL) proposed. In general, single lighting (SL) methods cannot well represent the steel surface because the surface features are not uniform and strongly vary according to lighting conditions. In the DLSL method, defective regions are represented by a black and white pattern, regardless of shape, size, or orientation. Therefore, defects can be found by the black and white patterns in the corresponding images. Gabor filtering was used to find defective regions and reduce the false positive rates. To find the periodic candidates of defects, we process the period searching using manufacturing information. To identify periodic defects from among the defect candidates, we use "similarity of shapes" features with a support vector machine (SVM) classifier. The experimental results show that the proposed algorithm is effective at detecting periodic defects on the surface of thick plates.

show abstract

Section: Detection Of Periodic Defects Using Dual-light Switching Ligmentioning

confidence: 99%

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Moreover, the defective parts can be enhanced by multiplying the detail coefficients with the weighting factors w H j , w V j , and w D j [14]: Figure 7 shows the wavelet energy distribution obtained from the experimental samples. The experimental sample is composed of 20 images in which 10 images are corner cracks and the rest are scales.…”

Section: Wavelet Reconstructionmentioning

confidence: 99%

“…if T high > Cx; y; then B high x; y 1; else B high x; y 0; (13) if T low > Cx; y; then B low x; y 1; else B low x; y 0; (14) where Cx; y is the middle region image of Rx; y with 1600 × 200 pixels; the Cx; y is obtained by the following procedure:…”

Section: Binarizationmentioning

confidence: 99%

See 1 more Smart Citation

Defect detection for corner cracks in steel billets using a wavelet reconstruction method

et al. 2014

View full text Add to dashboard Cite

Presently, automatic inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steelmaking industry. In this paper, we propose a vision-based method for detecting corner cracks on the surface of steel billets. Because of the presence of scales composed of oxidized substances, the billet surfaces are not uniform and vary considerably with the lighting conditions. To minimize the influence of scales and improve the accuracy of detection, a detection method based on a visual inspection algorithm is proposed. Wavelet reconstruction is used to reduce the effect of scales. Texture and morphological features are used to identify the corner cracks among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective in detecting corner cracks on the surfaces of the steel billets.

show abstract

“…2) Song et al also proposed a wavelet-based prepossessing method for improving the detection capacity by replacing the approximation coefficients with a constant value and by multiplying the detail coefficients. 3) Baek et al proposed a polynomial approximation-based system to detect the Mura defects. 4) Zhang and Zhang proposed a fuzzy neural network to solve the difficulty in modeling Muras.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic Diffusion Filter Based Blob-Mura Defect Detection in Thin Film Transistor Liquid Crystal Display Panel

Song

Choi

2010

Jpn. J. Appl. Phys.

Self Cite

View full text Add to dashboard Cite

In this paper, an anisotropic diffusion filter was employed to extract a background image in a thin film transistor liquid crystal display (TFT-LCD) panel image. The background image extracted by an iterative filtering was simply subtracted from a test image to detect blob-Mura defects. To reduce a processing time, we simply modified a conventional anisotropic diffusion filter and evaluated its results. The black and white bob-Mura defects which were included in the same image could be detected separately using only threshold values. Through simulation it was verified that the proposed method has a superior capability of detecting blob-Mura defects. #

show abstract

Wavelet-Based Image Enhancement for Defect Detection in Thin Film Transistor Liquid Crystal Display Panel

Cited by 17 publications

References 3 publications

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

Detection of Periodic Defects Using Dual-Light Switching Lighting Method on the Surface of Thick Plates

Defect detection for corner cracks in steel billets using a wavelet reconstruction method

Anisotropic Diffusion Filter Based Blob-Mura Defect Detection in Thin Film Transistor Liquid Crystal Display Panel

Contact Info

Product

Resources

About