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Proceedings Paper

Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments
Author(s): Guoliang Yang; Fuyuan Peng; Xutao Li; Kun Zhao; Jingdong Chen
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Paper Abstract

In this paper, we analyzed the increment distribution and the self-similarity behavior of texture images of three kinds of particular underwater objects during the mineral hunting process. The experimental data has shown that the H exponent of real underwater natural texture is not a constant over all scale range, but a variable with respect to the measure scale or time index. In order to investigate the multi-scale self-similarity behavior of the objects, we had extended the traditional FBM so that the self-similarity parameter H is taken as a variable H(s) with respect to measure scale s. The class-separability of self-similarity feature is measured, and the feature selection criterion is given. Pattern classification simulation experimental results have shown the effectiveness of the selected feature set combining the self-similarity parameter HΔ(3), the variance D(HΔ) and the increment variance AD. The correct ratio is up to 96% on average, which can be used in automatic detection and recognition for AUVs to complete their tasks.

Paper Details

Date Published: 15 November 2007
PDF: 6 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678627 (15 November 2007); doi: 10.1117/12.749352
Show Author Affiliations
Guoliang Yang, Huazhong Univ. of Science and Technology (China)
Fuyuan Peng, Huazhong Univ. of Science and Technology (China)
Xutao Li, South China Univ. of Technology (China)
Kun Zhao, Huazhong Univ. of Science and Technology (China)
Jingdong Chen, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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