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- W1973325667 abstract "Cyanobacteria blooms are constantly observed in the coastal waters and pose an enormous threat to public health, economy and ecological environment. The characteristics of blue algal bloom images and feature extraction procedures are analyzed in this paper. The pixel value of Cyanobacteria blooms color images has a significant difference from normal coastal waters images, particularly those of Hue and Saturation. A new method is proposed for Cyanobacteria blooms detection using dynamic K-means algorithm. Experimental results demonstrate the excellent practicability of the proposed detection method. Based on the pixel statistics, it can achieve a highly successful probability of detecting bloom images. Therefore, the proposed detection method can be expected to classify and detect Cyanobacteria blooms in monitoring and forecasting systems." @default.
- W1973325667 created "2016-06-24" @default.
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- W1973325667 date "2012-02-01" @default.
- W1973325667 modified "2023-09-27" @default.
- W1973325667 title "Research on Dynamic K-Means Clustering Algorithm in Cyanobacteria Blooms Detection" @default.
- W1973325667 cites W2067339322 @default.
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- W1973325667 doi "https://doi.org/10.4028/www.scientific.net/amm.157-158.428" @default.
- W1973325667 hasPublicationYear "2012" @default.
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