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

摘要

Many of the numerous texture measurements are based on space-frequency signal decomposition; these include Gabor filters and wavelet-based methods. The discrete cosine transformation (DCT) extracts spatial-frequency (SF) components from a local image region. It is the basis for the JPEG image compression standard and has many fast algorithmic implementations. By using a sliding DCT we derive a SF representation for a region of interest (ROI) surrounding 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 coefficients for a texture with a certain SF will change as the transformation is moved over the texture. In order to circumvent this problem, we derive horizontal and vertical SF shift-insensitive measurements from DCT components. Examples are given which show how these DCT shift-insensitive (DCTSIS) descriptors can be used to classify textured image regions. Since a large number of image display, storage and analysis systems are based on DCT hardware and software, DCTSIS descriptors may be easily integrated into existing technology and highly useful.