A fast approach to tuning an adaptive mask for texture segmentation

Local textural features, generally in terms of texture energy, are extracted by linear filtering of an image with a set of N-coefficient zero-sum and symmetric convolution masks. If the texture energy is defined as a sum of square rather than an absolute value of the convolution between the mask and...

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Bibliographic Details
Main Authors: Lam, R.W.-K., Chi-Kwong Li, Wai-Kong Cheuk
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Data mining
Image edge detection
Image segmentation
Image texture analysis
Information filtering
Information filters
Maximum likelihood detection
Nonlinear filters
Pixel
Online Access:Request full text
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Description
Summary:Local textural features, generally in terms of texture energy, are extracted by linear filtering of an image with a set of N-coefficient zero-sum and symmetric convolution masks. If the texture energy is defined as a sum of square rather than an absolute value of the convolution between the mask and the textured image, the order of the average over a window of size W and the convolution may be interchanged. As a result, the computation time may be reduced by about 2W/N for general adaptive mask approaches that require tens of thousands of iterations during the training.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.1997.633053