Abstract
Contourlets have emerged as a new mathematical tool for image processing and provide compact and decorrelated image representations. Hidden Markov modeling (HMM) of contourlet coefficients is a powerful approach for statistical processing of natural images. In this paper, we extended the hidden Markov modeling framework to contourlets and combined hidden Markov trees (HMT) with hidden Markov model to form HMM-Contourlet HMT model. The model is used for block based multiresolution texture segmentation. The performance of the HMM-contourlet HMT texture segmentation method is compared with that of HMM-real HMT and HMM-complex HMT methods. The HMM-contourlet HMT method provides superior texture segmentation results and excellent visual performance at small block sizes.
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