Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments

Guoliang Yang; Fuyuan Peng; Xutao Li; Kun Zhao; Jingdong Chen

doi:10.1117/12.749352

15 November 2007 Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments

Guoliang Yang, Fuyuan Peng, Xutao Li, Kun Zhao, Jingdong Chen

Author Affiliations +

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 678627 (2007) https://doi.org/10.1117/12.749352
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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 H_Δ(3), the variance D(H_Δ) 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.

Citation Download Citation

Guoliang Yang, Fuyuan Peng, Xutao Li, Kun Zhao, and Jingdong Chen "Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 678627 (15 November 2007); https://doi.org/10.1117/12.749352

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KEYWORDS

Liquids

Statistical analysis

Image classification

Stochastic processes

Minerals

Statistical modeling

Image processing

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