SCALED AND ROTATED TEXTURE CLASSIFICATION USING A CLASS OF BASIS FUNCTIONS

Three classes of basis functions are considered for classifying scaled and rotated textured images. The first is the orthonormal, compactly supported Daubechies and the discrete Haar bases, the second is the bi orthogonal basis and the third is the non orthogonal Gabor basis. Text ures are scaled and rotated and the basis functions are used to expand them. Features are computed on a combination of inter-resolution coef ficients. Experimental results show that the Daubechies orthonormal ba sis perform weil in recognizing transformed textures, followed by the Haar basis. The concept of multiresolution representation and orthogon ality are shown to be useful for invariant texture classificaiton. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.