In many computer vision tasks

in order to improve the accuracy and robustness to the noise

wavelet analysis is preferred for its natural multi-resolution property.However

the wavelet representation suffers from the dependency on the starting point in shape matching.For overcoming the problem

the Zernike moments are introduced

and a novel Starting-Point-Independent wavelet coefficient shape matching algorithm is presented.The proposed matching algorithm firstly gains the object contours

and gives the translation and scale invariant object shape representation.The object shape representation is converted to dyadic wavelet representation by wavelet transform

and then the Zernike moments of wavelet representation in different scales are calculated.With respect to property of rotation invariant of Zernike moments

consider the Zernike moments as the feature vector to calculate the similarity between the object and template image

which overcoming the problem of dependency on starting point.The experimental results indicates that the proposed algorithm is efficient

precise

and robust.