Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Machine vision
Wavelet transform-based locally orderless images for texture segmentation
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
Defect detection on semiconductor wafer surfaces
Microelectronic Engineering
Clustered defect detection of high quality chips using self-supervised multilayer perceptron
Expert Systems with Applications: An International Journal
A multivariate statistical analysis of the developing human brain in preterm infants
Image and Vision Computing
Computer-Aided vision system for MURA-Type defect inspection in liquid crystal displays
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
PCB inspection for missing or misaligned components using background subtraction
WSEAS Transactions on Information Science and Applications
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Pattern Recognition
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Automated visual inspection, a crucial manufacturing step, has been replacing the more time-consuming and less accurate human inspection. This research explores automated visual inspection of surface defects in a light-emitting diode (LED) chip. Commonly found on chip surface are water-spot blemishes which impair the appearance and functionality of LEDs. Automated inspection of water-spot defects is difficult because they have a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the defect may fall across two different background textures, which further increases detection difficulties. The one-level Haar wavelet transform is first used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based principal component analysis (WPCA) and Hotelling statistic (WHS) approaches are respectively applied to integrate the multiple wavelet characteristics. Finally, the principal component analysis of WPCA and the Hotelling control limit of WHS individually judge the existence of defects. Experimental results show that the proposed WPCA method achieves detection rates of above 93.8% and false alarm rates of below 3.6%, and outperforms other methods. A valid computer-aided visual defect inspection system is contributed to help meet the quality control needs of LED chip manufacturers.