A coal-rock recognition method based on statistical modeling ofsteerable pyramidal decomposition coefficients
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摘要: 针对传统煤岩识别方法易受采煤工艺、煤层赋存条件等因素限制而普适性不强的问题，提出了一种基于可调控塔式分解系数统计建模的煤岩识别方法。首先，对煤岩图像进行多尺度可调控塔式分解；然后，把非对称广义高斯分布作为一个统计模型去拟合每一个可调控方向子带的系数分布，采用最大似然估计法获得非对称广义高斯分布的各参数；最后，以对称的相对熵为距离测度完成煤岩图像的自动辨别。实验结果表明，与现有的其他方法相比，该方法的识别准确率最高，为86.90%。Abstract: For poor universal applicability of traditional coal-rock recognition methods whose application was limited by mining techniques and coal seam conditions, a coal-rock recognition method based on statistical modeling of steerable pyramidal decomposition coefficients was presented. Firstly, multi-scale steerable pyramidal decomposition was conducted on coal and rock images. Then, asymmetric generalized Gaussian distribution was adopted as a statistical model to fit coefficients of every steerable directional subband, and parameters of the asymmetric generalized Gaussian distribution were obtained by means of the maximum-likelihood estimation. Finally, symmetric relative entropy was employed as distance metrics to complete automatic identification of coal and rock images. The experimental results show that the method has high accuracy rate of coal-rock recognition than that of existing ones, which achieves 86.90%.
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