Zernike Moment Based Rotation Invariant Features For Patter Recognition

This paper addresses the problem of rotation invariant recognition of images. A new set of rotation invariant features are introduced. They are the magnitudes of a set of orthogonal complex moments of the image known as Zernike moments. A systematic reconstruction-based method for deciding the highest order of Zernike moments required in a classification problem is developed. The "quality" of the reconstructed image is examined through its comparison with the original one. More moments are included until the reconstructed image from them is close enough to the original picture. The orthogonality property of the Zernike moments which simplifies the process of image reconstruction makes the suggested feature selection approach practical. Features of each order are also weighted according to their contribution (their image representation ability) to the reconstruction process. This contribution is measured by comparing the difference between the original and the reconstructed image using these features with that obtained by using features of one less order. The application to a 26-class character data set yields 97% classification accuracy.