Recognition of 2D Object Contours Using Starting-Point-Independent Wavelet Coefficient Matching

In this paper, a new recognition algorithm for 2D object contours, based on the decimated wavelet transform, is presented, emphasizing the starting point dependency problem. The proposed matching algorithm consists of two parts: Firstly, we present new data structures for the decimated wavelet representation and a searching algorithm to estimate the misalignment between the starting points for the reference model and unknown object. We also adopt a polynomial approximation technique and propose a fast searching algorithm. And then, matching is performed in an aligned condition on the multiresolutional wavelet representation. By employing a variable-rate decimation scheme, we can achieve fast and accurate recognition results, even in the presence of heavy noise. We provide an analysis on the computational complexity, showing that our approach requires only less than 25% of the computational load required for the conventional method 1]. Various experimental results on both synthetic and real imagery are presented to demonstrate the performance of the proposed algorithm. The simulation results show that the proposed algorithm successfully estimates the misalignment and classifies 2D object contours, even for the input SNR = 5 dB.