Automated visual inspection: 1981 to 1987
Computer Vision, Graphics, and Image Processing
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
A survey of automated visual inspection
Computer Vision and Image Understanding
Machine Learning
Automated inspection of printed circuit boards through machine vision
Computers in Industry
Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
Digital Image Processing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance-Based Material Classification for Printed Circuit Boards
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Automatic Inspection System for Printed Circuit Boards
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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With the large variations in appearance for different kinds of defects in Printed Circuit Boards (PCBs), conventional rule-based inspection algorithms become insufficient for detecting and classifying defects. In this study, an automated PCB inspection system based on statistical learning strategies is developed. First, the partial Hausdorff distance is used to ascertain the positions of defects. Next, the defect patterns are categorized using the Support Vector Machine (SVM) classifier. Defects without regularities in appearance, which cannot be categorized, are identified through the regional defectiveness by comparing the block-wise probability distributions. Experimental results on a real visual inspection platform show that the proposed system is very effective for inspecting a variety of PCB defects.