PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Improvement of reliability in banknote classification using reject option and local PCA
Information Sciences—Informatics and Computer Science: An International Journal
Single-Class Classification with Mapping Convergence
Machine Learning
Neural Computation
Application of LVQ to novelty detection using outlier training data
Pattern Recognition Letters
Focusing on non-respondents: Response modeling with novelty detectors
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new method to identify the authenticity of banknotes based on the texture roughness
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Employing multiple-kernel support vector machines for counterfeit banknote recognition
Applied Soft Computing
SOM-based novelty detection using novel data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Recognition of Mexican banknotes via their color and texture features
Expert Systems with Applications: An International Journal
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This paper describes the neural-based recognition and verification techniques used in a banknote machine, recently implemented for accepting paper currency of different countries. The perception mechanism is based on low-cost optoelectronic devices which produce a signal associated with the light refracted by the banknotes. The classification and verification steps are carried out by a society of multilayer perceptrons whose operation is properly scheduled by an external controlling algorithm, which guarantees real-time implementation on a standard microcontroller-based platform. The verification relies mainly on the property of autoassociators to generate closed separation surfaces in the pattern space. The experimental results are very interesting, particularly when considering that the recognition and verification steps are based on low-cost sensors