| Summary: | 碩士 === 國立交通大學 === 電信研究所 === 83 === These thesis proposes a scale-invariant texture classification
scheme by using directional subband decomposition. The decompo-
sition is 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 recon- structed from these decimated ones. In each
decomposed channel image, we derive scale-invariant features
which correspond to the normalized power and the normalized
correlations. Training images are used to find feature
templates. During classification , the unknown texture is
matched against all the templates and the best match is taken
as the classification result. From simu- lations, we find that
the highest classification rate using 16 band decomposition for
16 kinds of texture is 98%.
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