Title :
Analysis of multiscale texture segmentation using wavelet-domain hidden Markov models
Author :
Choi, Hyeokho ; Hendricks, Brent ; Baraniuk, Richard
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Abstract :
Wavelet-domain hidden Markov tree (HMT) models are powerful tools for modeling the statistical properties of wavelet transforms. By characterizing the joint statistics of the wavelet coefficients, HMTs efficiently capture the characteristics of a large class of real-world signals and images. In this paper, we apply this multiscale statistical description to the texture segmentation problem. We also show how the Kullback-Leibler (KL) distance between texture models can provide a simple performance indicator.
Keywords :
discrete wavelet transforms; hidden Markov models; image segmentation; image texture; quadtrees; statistical analysis; Kullback-Leibler distance; image texture segmentation; multiscale texture segmentation; real-world signals; statistical properties; wavelet transforms; wavelet-domain hidden Markov tree models; Discrete wavelet transforms; Hidden Markov models; Image segmentation; Pixel; Shape; Statistics; Time measurement; Wavelet analysis; Wavelet coefficients; Wavelet transforms;
Conference_Titel :
Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-5700-0
DOI :
10.1109/ACSSC.1999.831914
