Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated machine learning approaches for complementing statistical process control procedures
Decision Support Systems
Wavelet transform-based locally orderless images for texture segmentation
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
Novelty detection for the inspection of light-emitting diodes
Expert Systems with Applications: An International Journal
Flaw detection of domed surfaces in LED packages by machine vision system
Expert Systems with Applications: An International Journal
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This research explores the automated visual inspection of surface blemishes that fall across two different background textures in a light-emitting diode (LED) chip. Water-drop defects, commonly found on chip surface, impair the appearance of LEDs as well as their functionality and security. Automated inspection of a water-drop defect is difficult because the blemish has a semi-opaque appearance and a low intensity contrast with the rough exterior of the LED chip. Moreover, the blemish may fall across two different background textures, which further increases the difficulties of defect detection. The one-level Haar wavelet transform is used to decompose a chip image and extract four wavelet characteristics. Then, wavelet-based neural network (WNN) and wavelet-based multivariate statistical (WMS) approaches are proposed individually to integrate the multiple wavelet characteristics. Finally, the back-propagation algorithm of WNN and T^2 test of WMS individually judge the existence of water-drop defects. Experimental results show that both of the proposed methods achieve above 95% and 92% detection rates and below 7.5% and 5.8% false alarm rates, respectively.