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Abstract: Wavelet-domain hidden Markov models (HMMs), in particular the hidden Markov tree (HMT), were recently proposed and applied to image processing. In this paper, we develop a new wavelet-domain HMM, the 2-D HMT-3, to fully capture the cross-correlations of the wavelet transform. The proposed 2-D HMT-3 is applied to texture analysis and synthesis based on the maximum likelihood criterion. For texture analysis, we study four wavelet-based texture classification methods, and the experimental results... (Update)
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BibTeX entry: (Update)
@misc{ fan-texture,
author = "Guoliang Fan and Xiang-Gen Xia",
title = "Texture Analysis And Synthesis Using Wavelet-Domain Hidden Markov Models",
url = "citeseer.ist.psu.edu/395941.html" }
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90 Adaptive Bayesian wavelet shrinkage (context) - Chipman, Kolaczyk et al. - 1997
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51 A parametric texture model based on joint statistics of comp.. - Portilla, Simoncelli - 2000
42 Real-time recognition with the entire Brodatz texture databa.. - Richard, Kabir et al. - 1993
40 Visual pattern discrimination (context) - Julesz - 1962
28 Bayesian tree-structured image modeling using wavelet-domain.. - Romberg, Choi et al. - 1999
27 A unified texture model based on a 2-d Wold-like decompositi.. (context) - Francos, Meiri et al. - 1993
27 An information-theoretic view of analog representation in st.. (context) - Daugman - 1990
22 Image segmentation using wavelet-domain classification - Choi, Baraniuk - 1999
20 Texture classification using QMF bank-based subband decompos.. (context) - Kundu, Chen - 1992
18 Introduction to optimization theory (context) - Gottfried - 1973
[Article contains additional citations not shown here]
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