A dyadic wavelet affine invariant function for 2D shape recognition

Dyadic wavelet transform has been used to derive an affine invariant function. First, an invariant function using two dyadic levels is derived. Then, this invariant function is used to derive another invariant function using six dyadic levels. We introduced the wavelet-based conic equation. The invariant function is based on analyzing the object boundary using the dyadic wavelet transform. Experimental results on both synthetic and real data are used to demonstrate the discriminating power of the proposed invariant function. It has also been compared with some traditional methods. The stability of the proposed invariant function is examined. In addition, the stability under large perspective transformation is tested.