The curvelet transform has the characteristic of anisotropy and the ability of good curve singularity expression. To overcome the shortage of invariant wavelet moments and Hu's invariant moments

curvelet transform is introduced into the wear particle feature extraction process and combined with Hu's invariant moments. Thus

an image invariant moment extracting method utilizing curvelet transform is proposed. First the wear particle images are decomposed and reconstructed by a curvelet transform

and their sub images of different scales are obtained. Then

the curvelet invariant moments are achieved by extracting Hu's invariant moments. Finally

the proposed method is applied for typical wear particle recognition

and a total successful recognition rate of 83.33% is accomplished. The experimental results indicate that compared with Hu's invariant moments and invariant wavelet moments

the curvelet invariant moments can better express wear particle appearance characteristics.