School of Mechanical Electronic & Information Engineering,China University of Mining & Technology (Beijing),Beijing　 100083,China

针对煤炭开采与加工过程中采煤机滚筒高度调节、选煤厂预排矸等工程实际问题,提出了1种有效的基于双树复小波域统计建模的煤岩识别方法。首先,通过双树复小波变换对煤岩图像进行多级分解;然后,提出了1种旋转不变增强策略,即对每1级双树复小波变换产生的高频子带按系数模的均值和方差之积从大到小排列;接着,提出了高频子带系数模符合广义伽玛分布模型的假设,并采用1种基于尺度独立形状估计方程的广义伽玛分布参数估计方法确定模型参数;最后,根据相对熵相似性测度完成煤岩图像的自动识别。结果表明:在双树复小波域中,广义伽玛分布模型具有较强的区分煤岩图像的能力;所提出的旋转不变增强策略在一定程度上提高了煤岩识别的正确识别率,并且使正确识别率与时间复杂度之间的折中权衡变得更加灵活;与现有的其他方法相比,所提出方法具有更高的正确识别率,其时间复杂度也是可以接受的。

In order to solve some practical engineering problems in coal mining and processing,such as the height ad- justment of shearer’s drum and preliminary gangue discharge in coal preparation plants,an effective approach to coal- rock recognition via statistical modeling in dual-tree complex wavelet domain was proposed. Firstly,coal or rock images were decomposed using multi-level dual-tree complex wavelet transform ( DTCWT). Secondly,a strategy for rotation- invariance enhancement was presented. According to this strategy,high-frequency sub-bands generated from every level DTCWT were sorted in descending order by the product of mean and variance of sub-band coefficient modulus. Third- ly,an assumption that generalized gamma distribution could be regarded as the underlying distribution of high-frequen- cy sub-band coefficient modulus was made,and the scale-independent shape estimation ( SISE) equations based pa- rameter estimation method was employed to determine the parameters of generalized gamma distribution. Finally,the automatic discrimination between coal and rock images was completed using similarity measurement with respect to rel- ative entropy. Experimental results demonstrate that the generalized gamma distribution statistical model has strong power to distinguish between coal and rock images. To some extent,the strategy presented for rotation-invariance en- hancement helps to increase the correct recognition rate of coal-rock recognition,and it is much more flexible to make a trade-off between correct recognition rate and time complexity by means of this strategy. The proposed approach outperforms several other existing ones in terms of correct recognition rate,the time complexity of which is still acceptable. This study could provide some guidance and reference for future practice on unmanned coal mining.

国家自然科学基金重点资助项目(51134024)；国家高技术研究发展计划(863)资助项目(2012AA062203)；

Sun Jiping,Chen Bang. An approach to coal-rock recognition via statistical modeling in dual-tree complex wavelet domain[ J]. Journal of China Coal Society,2016,41(7):1847-1858.