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Comparison of shape features for the classification of wear particles
Wear particle shapes are divided into four classes: regular, irregular, circular and elongated. They have been classified using backpropagation neural networks which have been trained using different sets of rotation-, scale- and translation-invariant shape features derived from particle boundaries. The features include: Fourier coefficients based on either boundary curvature analysis or XY co-ordinates of boundary points; statistical moments of the curvature distribution (including standard deviation, skewness and kurtosis); and two general shape descriptions (aspect ratio and roundness). In order to evaluate the performances of the features, a series of tests have been carried out on a wear particle database, and the results are compared. The boundary-curvature-based Fourier descriptors produce a shape classifier with the highest performance. The neural network trained by the Fourier features derived from the boundary data provides a slightly lower classification rate which is similar to that achieved using three statistical moments combined with the two general shape features.
Comparison of shape features for the classification of wear particles
Wear particle shapes are divided into four classes: regular, irregular, circular and elongated. They have been classified using backpropagation neural networks which have been trained using different sets of rotation-, scale- and translation-invariant shape features derived from particle boundaries. The features include: Fourier coefficients based on either boundary curvature analysis or XY co-ordinates of boundary points; statistical moments of the curvature distribution (including standard deviation, skewness and kurtosis); and two general shape descriptions (aspect ratio and roundness). In order to evaluate the performances of the features, a series of tests have been carried out on a wear particle database, and the results are compared. The boundary-curvature-based Fourier descriptors produce a shape classifier with the highest performance. The neural network trained by the Fourier features derived from the boundary data provides a slightly lower classification rate which is similar to that achieved using three statistical moments combined with the two general shape features.
Comparison of shape features for the classification of wear particles
Kun Xu (author) / Luxmoore, A.R. (author) / Deravi, F. (author)
Engineering Applications of Artificial Intelligence ; 10 ; 485-493
1997
9 Seiten, 12 Quellen
Article (Journal)
English
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