15 November 2007 Research on benthic scene recognition using multi-scale self-similarity model and statistical analysis of increments
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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 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.
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Guoliang Yang, Fuyuan Peng, Xutao Li, Kun Zhao, 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); doi: 10.1117/12.749352; https://doi.org/10.1117/12.749352
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