Color co-occurrence matrix based froth image texture extraction for mineral flotation
Color co-occurrence matrix based froth image texture extraction for mineral flotation
复制标题
基于颜色共生矩阵的矿物浮选泡沫图像纹理提取
DOI：
10.1016/j.mineng.2013.03.024
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发表时间：
2013-06-01
影响因子：
4.8
通讯作者：
Liao, Xi
中科院分区：
文献类型：
--
作者：
Gui, Weihua;Liu, Jinping;Liao, Xi
人们普遍认为，浮选泡沫的表面结构外观包含了有关其分离过程的关键信息，可以作为定性评价浮选性能的有效标准。为了获得不同生产条件下泡沫表面外观的明显特征，提出了一种基于颜色共生矩阵(CCM)的纹理特征提取方法，并与常用的灰度共生矩阵(GLCM)进行了比较。首先，使用HIS(色调、饱和度和强度)颜色空间来表示和量化泡沫图像，与RGB(红、绿、蓝)颜色空间相比，该颜色空间可以更直观地描述泡沫图像的颜色属性。然后，基于所提出的矩阵，计算泡沫表面纹理的CCM并提取相应的特征统计量。其次，在上述纹理特征统计的基础上，定义并提取了一个新的特征参数来描述泡沫纹理的复杂性。通过对中国某铝土矿浮选厂不同生产状态下的泡沫图像及泡沫中相应精矿品位的人工测定，研究了泡沫结构复杂性与相应精矿品位之间的定性关系。从而为进一步研究浮选过程的最优控制提供了满足生产指标要求的最佳织构复杂性范围。实验结果验证了该方法的有效性，并与以往基于GLCM的纹理特征提取方法进行了比较，证明了其优越性。(C)2013爱思唯尔有限公司。保留所有权利。
It is well accepted that the surface texture appearance of the flotation froth involves crucial information about its separation process, which can be used as an effective criterion for the qualitative assessment of the flotation performance. To obtain the distinctive characteristic of the froth surface appearance under various production conditions, a texture feature extraction method based on color co-occurrence matrix (CCM) is presented compared to the commonly used gray level co-occurrence matrix (GLCM). First, the HIS (Hue, Saturation and Intensity) color space is employed to exhibit and quantify the froth image, which yields a more intuitive description of the color properties in comparison with the RGB (Red, Green and Blue) color space. Then, the CCM is computed and the corresponding feature statistics of the froth surface texture are extracted based on the proposed matrix. Next, a new feature parameter is defined and extracted to describe the froth texture complexity based on the aforementioned texture feature statistics. After adequate offline froth images have been obtained from a bauxite flotation plant located in China under various production statuses with the corresponding concentrate grade in the froth assayed manually, the qualitative relationship between the texture complexity and the corresponding concentrate grade is investigated. Consequently, the optimal texture complexity range to achieve satisfactory production index is obtained for the further research of the optimal control of the flotation process. Experimental results have verified the effectiveness of the method and demonstrated its superiority over the previous texture feature extraction methods based on GLCM. (C) 2013 Elsevier Ltd. All rights reserved.