Automatic Solder Joint Inspection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tiered-Color Illumination Approach for Machine Inspection of Solder Joints
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining the results of several neural network classifiers
Neural Networks
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for combining classifiers employing shared and distinct pattern representations
Pattern Recognition Letters - special issue on pattern recognition in practice V
Data equalisation with evidence combination for pattern recognition
Pattern Recognition Letters
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
On naive Bayesian fusion of dependent classifiers
Pattern Recognition Letters
Computational Statistics Handbook with MATLAB, Second Edition (Chapman & Hall/Crc Computer Science & Data Analysis)
Classifier ensembles: Select real-world applications
Information Fusion
A Bayesian framework for multilead SMD post-placement quality inspection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Ensemble-Based Incremental Learning Approach to Data Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comparing classifiers and metaclassifiers
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
A multilevel information fusion approach for visual quality inspection
Information Fusion
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
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Current trends in the electronics industry are towards miniaturization of components, denser packing of printed-circuit boards and highly automated assembly lines. The technology of Surface Mounted Devices (SMD) facilitates this trend, thus explaining the substantial increase in the use of its various versions. Nevertheless, dense packaging requires increased accuracy in the placement and efficient inspection of components in order to ensure high reliability in manufacturing. This paper presents fusion methods of multiple classifiers for improving the classification of individual components in terms of positioning accuracy through computer vision inspection. Multiple classifier combination is a technique that combines the decisions of different classifiers as to reduce the variance of estimation errors and improve the overall classification accuracy. Combining the power of the primary classifiers through multi-modular architectures improves the classification results and contributes to the robustness of the overall inspection system.