Wavelet neural network for 2D object classification

In this paper, a wavelet neural network (WNN)-based approach for invariant 2D object classification is proposed. The method employs the WNN characterizing the singularities of the object curvature representation and performing the classification at the same time and in an automatic way. The discriminative time-frequency attributes of the singularities on the object boundary are firstly captured by the continuous wavelet transform (CWT) and then stored by the WNN as its initial scale-translation parameters. These parameters are trained to the optimum status during the learning stage. Thus, only a few convolutions at the optimum scale-translation grids are involved during the classification, which makes our method suitable for real-time recognition tasks. Compared with the artificial neural network (ANN)-based approach preceded by a wavelet filter bank with fixed scale-translation parameters as well as the traditional methods like Fourier descriptors and moment invariants, our scheme demonstrates the best discrimination performance under various noisy and affine conditions.