15 November 2007 Unsupervised texture segmentation based on nonsubsampled contourlet and a novel artificial immune network
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Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 678624 (2007); doi: 10.1117/12.749347
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
This paper describes a novel structural adaptation artificial immune network (SAAN) clustering algorithm for texture segmentation. In the SAAN, a new immune antibody neighborhood and an adaptive learning coefficient are presented. The model can adaptively map input data into the antibody output space, which has a better adaptive net structure. Images are first partitioned into a set of regions by using the watershed segmentation. Then the nonsubsampled contourlet texture features are extracted from each watershed region as the antigens of the SAAN. Finally the antibodies clustering results of the SAAN are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with various texture images illustrate the effectiveness of the proposed novel segmentation algorithm.
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Wenlong Huang, Licheng Jiao, "Unsupervised texture segmentation based on nonsubsampled contourlet and a novel artificial immune network", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678624 (15 November 2007); doi: 10.1117/12.749347; https://doi.org/10.1117/12.749347
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KEYWORDS
Image segmentation

Silver

Atrial fibrillation

Feature extraction

Image processing algorithms and systems

Data modeling

Synthetic aperture radar

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