K. Xu et al., COMPARISON OF SHAPE-FEATURES FOR THE CLASSIFICATION OF WEAR PARTICLES, Engineering applications of artificial intelligence, 10(5), 1997, pp. 485-493
Wear particle shapes are divided into four classes: Regular; irregular
Circular and Elongated. They have been classified here using back-pro
pagation neural networks which have been trained using different see o
f rotation-, scale-and translation-invariant shape features derived fr
om particle boundaries. The features include: Fourier coefficients bas
ed on either boundary curvature analysis or XY co-ordinates of boundar
y points; statistical moments of the curvature distribution including
standard deviation, skewness and kurtosis; and two general shape descr
iptions, aspect ratio and roundness. In order to evaluate the performa
nces of the features, a series of tests have been carried out on a wea
r particle database, and the results are compared. The boundary-curvat
ure-based Fourier descriptors produce a shape classifier with the high
est performance. The neural network trained by the Fourier features de
rived 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. (C) 1997 Elsevier Scien
ce Ltd. All rights reserved.