A Multi-Resolution Feature Reduction Technique for Image Segmentation with Multiple Components
M. Unser, M. Eden
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'88), Ann Arbor MI, USA, June 5-9, 1988, pp. 568–573.
The authors present a linear feature-reduction technique for multicomponent or textured image segmentation. The transformation matrix is computed by simultaneously diagonalizing scatter matrices evaluated at two different spatial resolutions. Under reasonable conditions, this transform closely approximates the generalized Fisher linear discriminants which are optimal for region separability. Experimental examples suggest that this technique is superior to the Karhunen-Loève transform for texture segmentation.
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AUTHOR="Unser, M. and Eden, M.",
TITLE="A Multi-Resolution Feature Reduction Technique for Image
Segmentation with Multiple Components",
BOOKTITLE="Proceedings of the {IEEE} Computer Society Conference on
Computer Vision and Pattern Recognition ({CVPR'88})",
YEAR="1988",
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pages="568--573",
address="Ann Arbor MI, USA",
month="June 5-9,",
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