Record number :
396654
Title of article :
Multiscale image segmentation using wavelet-domain hidden Markov models
Author/Authors :
Choi، نويسنده , , H.، نويسنده , , Baraniuk، نويسنده , , R.G. ، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2001
Pages :
13
From page :
1309
To page :
1321
Abstract :
We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images without the need for decompression into the space domain. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.
Keywords :
Hidden Markov tree , segmentation , Texture modeling , wavelets.
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING
Serial Year :
2001
Link To Document :
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