Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Adaptive Texture and Shape Based Defect Classification
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Statistical Classification of Raw Textile Defects
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Automatic Defect Classification Using Boosting
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Selective removal of impulse noise based on homogeneity level information
IEEE Transactions on Image Processing
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We describe here a system, based on boosting, for the classification of defects on material running on a production line. It is constituted of a two-stages architecture: in the first stage a set of features are extracted from the images surveyed by a linear camera located above the material. The second stage is devoted to the classification of the defects from the features. The novelty of the system resides in the ability to rank the defects with respect to a set of classes, achieving a rate of identification of dangerous defects very close to 100%.