Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Wood inspection with non-supervised clustering
Machine Vision and Applications
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators
Machine Vision and Applications
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
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In this paper, we present a vision-based method for the automatic inspection of defects on ball bearings. A supervised neural network, employing a Multilayer Perceptron trained with the BackPropagation algorithm, is applied for discriminating defects. The images of the ball bearings are first mapped to a new space, called Defect Evaluation Space, to simplify the process of analyzing the images. The obtained images are then processed in the new space and wavelet based feature vectors are extracted. Different combinations of the wavelet coefficients are used as input vectors to the neural classifier in order to carefully select those that produce the best results. The false alarm rate, evaluated on a test set of images, is low demonstrating the accuracy and robustness of the proposed approach.