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

针对小波难以表达煤岩图像的边缘曲线特征,影响识别精度的问题,提出一种基于曲波变换的方法,对煤岩图像边缘进行稀疏表示。该方法通过曲波变换对煤岩图像进行曲波分解,得到各尺度层曲波系数,保留图像变换后的Coarse层低频系数,基于压缩感知理论,利用随机高斯矩阵对高频系数进行测量,实现高维系数降维,Coarse层低频系数与降维后的高频系数通过级联构成煤岩图像特征向量,最后结合支持向量机对煤岩图像进行分类识别。实验表明:通过曲波分解提取的特征能够有效地表达煤岩图像边缘的曲线特征,所提出方法煤岩的分类准确率达93.75%,比Haar小波方法提高了4.37%,所用降维方法比线性降维方法提取的特征向量更加有利于煤岩图像的分类识别。

As wavelet cannot well express the edge curve characteristics of coal-rock images,leading to a low recogni- tion accuracy,a method based on curvelet transform was proposed to have a sparse representation of coal-rock image edges. The method used the curvelet transform to decompose images into curvelet coefficients in different scales. The low-pass coarse coefficients were preserved and then Gaussian random matrices were used to measure the high-pass co- efficients in order to realize a dimensionality reduction based on the compressed sensing theory. The feature vectors for coal-rock images were created by concatenating the low-pass coarse coefficients and high-pass coefficients after dimen- sionality reduction. Finally,classification and identification were carried by support vector machine. Experimental re- sults showed that the features extracted by curvelet decomposition could effectively express the curve features of coal- rock image edges. The classification accuracy of the proposed method reached 93. 75% . And it improved the classifica- tion accuracy of 4. 37% than Haar wavelet method. The feature vectors extracted by the proposed dimensionality re- duction method were more advantageous to the classification and recognition of coal and rock images than the linear di- mensionality reduction methods.

国家重点研发计划资助项目(2016YFC0801800)；国家自然科学基金重点资助项目(51134024)；

Wu Yunxia,Zhang Hong. Recognition method of coal-rock images based on curvelet transform and compressed sensing[J]. Journal of Chi- na Coal Society,2017,42(5):1331-1338.