Multiscale Texture Segmentation of Dip-cube Slices using Wavelet-domain Hidden Markov Trees
van Spaendonck, Rutger
Baraniuk, Richard G.
segmentation; hidden markov trees
Wavelet-domain Hidden Markov Models (HMMs) are powerful tools for modeling the statistical properties of wavelet coefficients. By characterizing the joint statistics of wavelet coefficients, HMMs efficiently 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 classifier for textures. Utilizing the inherent tree structure of wavelet-domain HMM, classification 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 HMM. Based on the multiscale classification results obtained from the wavelet-domain HMM, we develop a method to combine the multiscale classification results to generate a reliable segmentation of the texture images. We apply this new technique to the segmentation of dip-cube slices.