MULTI-SCALE IMAGE SEGMENTATION BASED ON CONTOURLET-DOMAIN HIDDEN MARKOV TREES MODEL

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[2] Matthew S Crouse, Robert D Nowak, Richard G Baraniuk. Wavelet-based statistical signal processing using hidden markov models [J]. IEEE Transaction on Signal Processing, 1998,46(4):886-902.

[5] Candès E J. Ridgelet: theory and applications [D]. Ph.D. dissertation. Stanford Univ., 1998.

[6] Candès E J. Monoscale ridgelets for the representation of images with edges [R]. Technical report, Stanford Univ., 1999.

[7] Candès E J, Donoho D L. Curvelets [R]. Technical report, Stanford Univ., 1999.

[8] Do M N, Vetterli M. Contourlets: a new directional multiresolution image representation [C]. Signals, Systems and Computers, Conference Record of the Thirty-Sixth Asilomar Conference, Rochester:2002,1:3-6.

[9] Hyeokho Choi, Richard G Baraniuk. Multiscale image segmentation using wavelet-domain hidden Markov models [J]. IEEE Transactions on Image Processing, 2001,10(9):1309-1321.

[10] FAN Guo-Liang, XIA Xiang-Gen. A joint multicontext and multiscale approach to Bayesian image segmentation [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(12):2680-2688.

[11] SUN Qiang, GOU Shuiping, JIAO Licheng. A new approach to unsupervised image segmentation based on qavelet-domain hidden Markov tree models [C]. ICIAR2004, Portugal: Porto,2004:41-48.

[12] Duncan D Y Po, Do M N. Directional multiscale modeling of images using the contourlet transform [J]. Statistical Signal Processing, 2003 IEEE Workshop on, 2003:262-265.

[13] Bouman C A, Shapiro M. A multiscale random field model for Bayesian image segmentation [J]. IEEE Trans. Iimage Processing, 1994, 3(2):162-177.