计算机科学 ›› 2014, Vol. 41 ›› Issue (12): 293-296.doi: 10.11896/j.issn.1002-137X.2014.12.063
刘哲,宋余庆,包翔
LIU Zhe,SONG Yu-qing and BAO Xiang
摘要： 针对有参混合模型的聚类算法需要假设模型为某种已知的参数模型而存在模型不匹配及应用于图像分割时对噪声比较敏感的问题,提出了一种基于空间邻域信息的B样条密度模型的图像分割方法。首先,通过构建基于规范化的B样条密度函数的非参数混合模型,定义空间信息函数,使得分割模型具有空间邻域信息；其次,利用非参数B样条期望最大(NNBEM)算法估计密度模型的未知参数；最后根据贝叶斯准则实现图像的分割。该图像分割方法不需要假设图像符合某种模型,就可以克服实际数据分布与假设图像模型不一致的问题。此方法有效克服了“模型失配”问题,而且有力抑制了噪声点,同时很好地保留了边界的特性。分别对模拟图像进行仿真,验证了基于空间邻域信息的B样条密度模型的分割方法的有效性。
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