HMM-based multiresolution image segmentation

Published

Journal Article

A texture segmentation algorithm is developed, utilizing a wavelet-based multi-resolution analysis of general imagery. The wavelet analysis yields a set of quadtrees, each composed of high-high (HH), high-low (HL) and low-high (LH) wavelet coefficients. Hidden Markov trees (HMTs) are designed for the quadtrees. For a given texture we define a set of "hidden" states, and a hidden Markov model (HMM) is developed to characterize the statistics of a given quadtree with respect to the statistics of surrounding quadtrees. Each HMM state is characterized by a unique set of HMTs. An HMM-HMT model is developed for each texture of interest, with which image segmentation is achieved. Several numerical examples are presented to demonstrate the model, with comparisons to alternative approaches.

Duke Authors

Cited Authors

  • Lu, J; Carin, L

Published Date

  • July 11, 2002

Published In

Volume / Issue

  • 4 /

International Standard Serial Number (ISSN)

  • 1520-6149

Citation Source

  • Scopus