Multiscale texture segmentation of dip-cube slices using wavelet-domain hidden Markov trees

Wavelet-domain hidden Markov models (HMMs) are powerful tools for modeling the statistical properties of wavelet transforms. By characterizing the joint statistics of wavelet coe cients, HMMs e ciently capture the characteristics of many real-world signals. When applied to images, the model can characterize the joint statistics between pixels, providing a very good classi er for textures. Utilizing the inherent tree structure of waveletdomain HMMs, classi cation of textures at various scales is possible, furnishing a natural tool for multiscale texture segmentation. In this paper, we introduce a new multiscale texture segmentation algorithm based on wavelet-domain hidden Markov trees (HMTs). We apply this new technique to the segmentation of dip-cube time slices.