| Summary: | 碩士 === 國立交通大學 === 電信研究所 === 82 === This thesis presents both texture classification and
segmentation algorithms by multichannel decomposition. The
channels are characterized by a bank of directional subband
filters that allow a two-dimensional input signal to be
represented by a sum of maximally decimated subband images and
perfectly reconstructed from these decimated ones. For
classification, we model the filtered channel image as Markov
Random Field (MRF) and the model parameters are then extracted
as texture features. For texture segmentation, four stages are
taken, namely, features extraction, coarse segmentation, fine
segmentation, and post processing. Correlations in each channel
are used as features. At coarse segmentation stage, a fast and
efficient clustering algorithm by incorporating the clusters'
spatial locations is introduced. To estimate the true number of
textures (cluster validility problem), we propose a new cluster-
number decision algorithm by integrating each channel's
clustering result. Simulation results demonstrate the
effectiveness of our proposed texture classification and
segmentation algorithms.
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