Model-based recognition method for circular boundaries

Xian Zhao; Li Rong Cui

Model-based recognition method for circular boundaries

Xian Zhao^*, Li Rong Cui

^*此作品的通讯作者

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

To recognize circular boundaries, a mixed probability density function that can generate the circular spatial point pattern is built up. In terms of the above, the probability density function, a circular boundaries recognition method is constructed, It consists of three parts:viz.:denoising; estimating the parameters of mixed probability density function, and identifying the number of clusters via BIC. The new method overcomes the limitation of fuzzy C-shell clustering. simulation study provided some promising results.

源语言	英语
页（从-至）	843-846
页数	4
期刊	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
卷	28
期	9
出版状态	已出版 - 9月 2008

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引用此

Zhao, X., & Cui, L. R. (2008). Model-based recognition method for circular boundaries. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 28(9), 843-846.

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title = "Model-based recognition method for circular boundaries",

abstract = "To recognize circular boundaries, a mixed probability density function that can generate the circular spatial point pattern is built up. In terms of the above, the probability density function, a circular boundaries recognition method is constructed, It consists of three parts:viz.:denoising; estimating the parameters of mixed probability density function, and identifying the number of clusters via BIC. The new method overcomes the limitation of fuzzy C-shell clustering. simulation study provided some promising results.",

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AB - To recognize circular boundaries, a mixed probability density function that can generate the circular spatial point pattern is built up. In terms of the above, the probability density function, a circular boundaries recognition method is constructed, It consists of three parts:viz.:denoising; estimating the parameters of mixed probability density function, and identifying the number of clusters via BIC. The new method overcomes the limitation of fuzzy C-shell clustering. simulation study provided some promising results.

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KW - Classification expectation maximization method

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KW - Pattern recognition

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Model-based recognition method for circular boundaries

摘要

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