Multiscale contour corner detection based on local natural scale and wavelet transform

A new corner detection method for contour images is proposed based on dyadic wavelet transform (WT) at local natural scales. The points corresponding to wavelet transform modulus maxima (WTMM) at different scales are taken as corner candidates. For each candidate, the scale at which the maximum value of the normalized WTMM exists is defined as its ''local natural scale'', and the corresponding modulus is taken as its significance measure. This approach achieves more accurate estimation of the natural scale of each candidate than the existing global natural scale based methods. Furthermore, the proposed algorithm is suitable for both long contours and short contours. The simulation and the objective evaluation results reveal better performance of the proposed algorithm compared to the existing methods.