Water Surface Floater Clustering Based on Mean Shift

Peng Yang; You Fu Wu; Zu Sheng Chen; Xue Lin Wang; Ming Gui Zhang

doi:10.4028/www.scientific.net/AMR.926-930.3334

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Water Surface Floater Clustering Based on Mean...

Water Surface Floater Clustering Based on Mean Shift

Abstract:

That applying computer vision technology into the water surface floater intelligent monitoring is a creative and interesting topic. In this article, we propose the water surface floater clustering based on Mean Shift algorithm. Adopting RGB space color information, the image is divided into fixed size 3*3 small blocks, the block is mapped as vertex of graph, and each block pixel mean is mapped as pixel values of vertex; before using the Mean Shift clustering put forward carrying on one or more Mean Shift filtering smoothing process. Finally, each vertex of clustering is mapped to a 3*3 small blocks, block pixel values are vertex pixel values, and according to the proportion of all kinds of floater in the perceptual area, calculate its pollution degree, it is used to measure or evaluate the pollution index of water surface. It is showed that the method is effective and feasible by experiment.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3334-3337

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.3334

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Online since:

May 2014

Authors:

Peng Yang*, You Fu Wu, Zu Sheng Chen, Xue Lin Wang, Ming Gui Zhang

Keywords:

Clustering, Floater, Mean-Shift, Pollution Degree

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* - Corresponding Author

References

[1] T. Collins, A.J. Lipton, T. Kanade: Introduction to the Spectial Section on Video Surveillance, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22 (2000), No. 8, , pp.745-746.

DOI: 10.1109/tpami.2000.868676

Google Scholar

[2] S.J. Maybank, T.N. Tan: Special section on visual surveillance-introduction, International Journal of Computer Vision, Vol. 37 (2000), No. 2, pp.173-174.

Google Scholar

[3] J. Jiang, G. Li: Yellow River, Vol. 32 (2010), No. 11, pp.47-48.

Google Scholar

[4] Y.F. Wu, J.J. Zuo, J. Wu: Environmental Science and Technology, Vol. 18 (2012), No. 2, pp.1-4.

Google Scholar

[5] Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Trans on Information Theory, Vol. 21 (1975), No. 1, pp.32-40.

DOI: 10.1109/tit.1975.1055330

Google Scholar

[6] Cheng Yizong: Mean shift, mode seeking, and clustering, IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 17 (1995), No. 8, pp.790-799.

DOI: 10.1109/34.400568

Google Scholar