C4.5: programs for machine learning
C4.5: programs for machine learning
Combining the results of several neural network classifiers
Neural Networks
A survey of automated visual inspection
Computer Vision and Image Understanding
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Automatic PCB inspection algorithms: a survey
Computer Vision and Image Understanding
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ACM Computing Surveys (CSUR)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Fusion
A system for real-time fabric inspection and industrial decision
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Exploiting Symbolic Learning in Visual Inspection
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Sensor and Data Fusion: A Tool for Information Assessment and Decision Making (SPIE Press Monograph Vol. PM138)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
On the Bayes fusion of visual features
Image and Vision Computing
Visual inspection of machined metallic high-precision surfaces
EURASIP Journal on Applied Signal Processing
Extensions of vector quantization for incremental clustering
Pattern Recognition
Adaptive mixtures of local experts
Neural Computation
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
Machine Vision and Applications
Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Towards incremental classifier fusion
Intelligent Data Analysis
A new method of feature fusion and its application in image recognition
Pattern Recognition
A novel feature selection based semi-supervised method for image classification
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiple classifier system for early melanoma diagnosis
Artificial Intelligence in Medicine
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For visual quality inspection systems to be applicable in industrial settings, it is mandatory that they are highly flexible, robust and accurate. In order to improve these characteristics a multilevel information fusion approach is presented. A first fusion step at the feature-level enables the system to learn from an undefined number of potential defects which might be segmented from the images. This allows for the quality control operators to label the data at the image-level and the sub-image-level, and use this information during the learning process. Additionally, the operators are allowed to provide a confidence measure for their labelling. The additional information obtained from the increased flexibility of the operator inputs allows to build more accurate classifiers. A second fusion step at the decision-level combines the classifications of different classifiers, making the system more accurate and more robust with respect to the classification method chosen. The experimental results, using various artificial and real-world visual quality inspection data sets, show that each of these fusion approaches can significantly improve the classification accuracy. If both information fusion approaches are combined the accuracy increases even further, significantly outperforming each of the fusion approaches on their own.