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ASME Press Select Proceedings
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)
Editor
Garry Lee
Garry Lee
Information Engineering Research Institute
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ISBN:
9780791859896
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
143 Target Detection Using Combined Gaussian Model and Correlation Coefficients
By
Ting Rui
Engineering Institute of Engineering Corps, PLA University of Science and Technology , Nanjing , China
,
Ting Rui
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Jian-chun Xing
Engineering Institute of Engineering Corps, PLA University of Science and Technology , Nanjing , China
,
Jian-chun Xing
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Jing-wei Zhu
Engineering Institute of Engineering Corps, PLA University of Science and Technology , Nanjing , China
,
Jing-wei Zhu
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Jin-lin Zhang
Engineering Institute of Engineering Corps, PLA University of Science and Technology , Nanjing , China
,
Jin-lin Zhang
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Hua-bing Li
Engineering Institute of Engineering Corps, PLA University of Science and Technology , Nanjing , China
Hua-bing Li
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Page Count:
4
-
Published:2011
Citation
Rui, T, Xing, J, Zhu, J, Zhang, J, & Li, H. "Target Detection Using Combined Gaussian Model and Correlation Coefficients." International Conference on Mechanical Engineering and Technology (ICMET-London 2011). Ed. Lee, G. ASME Press, 2011.
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Single Gaussian model is an effective way for target detection under stable environment. However, it suffers from low robustness when there are dynamic scenes and/or sudden lighting changes. The correlation coefficients method is effective in describing the similarity between images. Furthermore, this method is not sensitive to small image appearance changes. To take advantage of this characteristic, a hierarchical block detection mechanism is proposed in this paper. First, the noise from the dynamic background scenes and/or the lighting changes are filtered by the correlation coefficients. After that, the blocks with foreground are segmented by single Gaussian model. Experiments confirmed that...
Abstract
Keywords
Introduction
Background Modeling of Gaussian Model
Foreground Detection Based on Correlation Coefficient
Implementation of Algorithm and the Analysis of Experimental Results
Conclusion
Acknowledgment
References
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