Ivan Magrin-Chagnolleau, Hyeokho Choi, Rutger van Spaendonck, Philippe Steeghs and Richard G. Baraniuk.
Multiscale texture segmentation of dip-cube slices using Wavelet-domain hidden Markov trees.
Proceedings of the 69th SEG Meeting, Houston, Texas, USA, 1999.

Abstract: 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.