A filter bank for rotationally invariant image recognition

We present new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering methodology combined with the Mahalanobis distance measure. The proposed procedure verifies an impact of random noise as well as an interesting, less known impact of noise due to spatial transformations. The recognition accuracy of the proposed technique has been tested with the Zernike moments, the Fourier–Mellin moments as well as with wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low level noise, 8% for the average level noise and 15% for high level noise.