Frame representations for texture segmentation
We introduce a novel method of feature extraction for texture segmentation that relies on
multichannel wavelet frames and 2-D envelope detection. We describe and compare two
algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings.
We present criteria for filter selection and discuss quantitatively their effect on feature
extraction. The performance of our method is demonstrated experimentally on samples of
both natural and synthetic textures.
multichannel wavelet frames and 2-D envelope detection. We describe and compare two
algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings.
We present criteria for filter selection and discuss quantitatively their effect on feature
extraction. The performance of our method is demonstrated experimentally on samples of
both natural and synthetic textures.
We introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2-D envelope detection. We describe and compare two algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings. We present criteria for filter selection and discuss quantitatively their effect on feature extraction. The performance of our method is demonstrated experimentally on samples of both natural and synthetic textures.
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