Texture discrimination using discrete cosine transformation shift-insensitive (DCTSTS) descriptors

Many of the numerous texture measurements are based on space-frequency sign al decomposition; these include Gabor filters and wavelet-based methods. Th e discrete cosine transformation (DCT) extracts spatial-frequency (SF) comp onents from a local image region. It is the basis for the JPEG image compre ssion standard and has many fast algorithmic implementations. By using a sl iding DCT we derive a SF representation for a region of interest (ROI) surr ounding each image pixel. We show that the DCT coefficients may represent a SF as a combination of several DCT coefficients depending on the offset of the SF waveform maximum from the ROI's beginning, Thus, the DCT coefficien ts for a texture with a certain SF will change as the transformation is mov ed over the texture. In order to circumvent this problem, we derive horizon tal and vertical SF shift-insensitive measurements from DCT components, Exa mples are given which show how these DCT shift-insensitive (DCTSIS) descrip tors can be used to classify textured image regions. Since a large number o f image display, storage and analysis systems are based on DCT hardware and software, DCTSIS descriptors may be easily integrated into existing techno logy and highly useful. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.