煤矸区分中的间隔灰度压缩扩阶共生矩阵

Expanded order co-occurrence matrix to differentiate between coal and gangue based on interval grayscale compression

为了有效提高共生矩阵惯性矩在图像纹理分析及检索中的作用,在常规纹理分析方法的基础上,研究了一种基于间隔灰度压缩的扩阶共生矩阵惯性矩。该方法采用对原图像的灰度信息进行部分压缩和部分保留的综合处理方式,并通过矩阵的扩阶提取未压缩的灰度信息,达到灰度信息随机与有序兼容利用的效果。实验结果表明,该算法比基于图像全局灰度压缩的常规共生矩阵惯性矩具有更大的目标类型区分度,其目标区分正确率大于82%,也更容易设置相应的区分阈值,而且具有较高的图像分析实时性。

In order to play the role of co-occurrence matrix inertia in the analysis and retrieval of image texture efficient-ly, a new expanded order co-occurrence matrix inertia based on interval grayscale compression is studied. One part of the grayseale information of the original image is Compressed and another is retained in this integrated approach. The uncom- pressed grayscale information is extracted by an order expansion of the matrix. The effects of the grayscale information are used randomly. Experimental results show that the algorithm differentiaties the target types better than the conventional co-occurrence matrix based algorithms. More than 82% of the objects are differentiated correctly, and the methods ap- propriate distinction threshold is easier to set and faster.