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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Published in1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation Vol. 4; pp. 3042 - 3045 vol.4
Main Authors Lam, R.W.-K., Chi-Kwong Li, Wai-Kong Cheuk
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Convolution
Data mining
Image edge detection
Image segmentation
Image texture analysis
Information filtering
Information filters
Maximum likelihood detection
Nonlinear filters
Pixel
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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.
ISBN:0780340531
9780780340534
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.1997.633053