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.
|Original language||English (US)|
|Title of host publication||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Jan 1 2002|