基于模型的圆形边界识别方法

Model-Based Recognition Method for Circular Boundaries

  • 摘要: 为了有效识别圆形边界,构造了圆形空间点模式产生的概率密度函数,并以此概率密度函数为基础,建立了由噪声点去除、混合概率密度函数参数估计与基于BIC的聚类个数自动判别3部分组成的圆形边界识别方法. 该方法克服了模糊C球壳聚类算法的缺点. 仿真结果表明,该方法能有效地识别圆形边界.

     

    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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