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　　图3 灰度共生矩阵特征参数
　　Fig.3 Features of GLCM
　　6 结论
　　基于灰度共生矩阵的纹理特征参数能较好的描述回转窑的熟料表面纹理,利用Fisher系数提取出最佳位置算子和最有效分类参数集能有效的避免传统方法中计算灰度共生矩阵的随意性和大计算量,既保证了分类识别的精度,又充分考虑了在线识别的实时性。基于约简的纹理特征参数组,经典的C4.5决策树算法实现了三种烧结状态下的熟料的分类,分类精度达到95.65%,这充分说明了用纹理分析方法进行熟料烧结状态判别的可行性。
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