【摘要】 针对现有算法对不规则封闭图形轮廓特征辨识效率不高的问题,提出一种基于自定义等高测度矩阵的快速辨识算法.自定义等高测度矩阵从轮廓形态上最优地描述了不规则封闭图形的几何特征;基于该矩阵自定义图形单元的偏心率族参数,并将该参数的概率密度函数作为样本分类器设计依据;结合Bayes理论提出了最小误差概率分类器的设计方法.实际应用验证表明,该算法对轮廓特征的表述具有较好的平移、尺度不变性,且具有30倍于傅里叶描述子的运算效率.更多还原

【Abstract】 For higher efficiency of irregular-closed graphic recognition than the traditional algorithms,a self-defined contour measurement matrix is introduced,and based on which a fast recognition algorithms is proposed. The contour matrix optimally describes the geometric characteristic of the graphic outline,then a group of eccentric-rate parameters is defined based on the matrix,and the probability density function of the parameters is considered as the basis of classification. Combined with Bayes theory,a design method of classification with minimum-error probability (MEP) is proposed. The proposed algorithm is demonstrated by applications to be invariant to translation and scaling,is about 30 times more computationally efficient than the Fourier-based descriptors.更多还原