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 in | 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation Vol. 4; pp. 3042 - 3045 vol.4 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
1997
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 0780340531 9780780340534 |
ISSN: | 1062-922X 2577-1655 |
DOI: | 10.1109/ICSMC.1997.633053 |