ROTATION INVARIANT IMAGE RECOGNITION USING FEATURES SELECTED VIA A SYSTEMATIC METHOD

In this paper a new set of rotation invariant features for image recognition is introduced. The features are the magnitudes of a set of orthogonal complex moments of the image called Zernike moments. These features could easily be constructed to an arbitrary high order. Taking advantage of the orthogonality property, a systematic feature selection method for choosing an appropriate number of the Zernike features is developed. It is based on computing a measure of the information content differences of features of different classes. The performance of the method is experimentally tested on a 26-class and a 10-class data set involving differently oriented binary images. The first set consists of 624 images of all English characters. The second one is an extensive set of numeric handprinted characters involving 16,550 samples. Using a nearest neighbor classifier 99% and 84% classification accuracies are obtained respectively. © 1990.