浮选泡沫精矿品位图像识别研究
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摘要
本文综述了数字图像技术的发展及应用；简述了国内外浮选泡沫数字图像处理技术的应用及算法研究现状，同时指出其应用的发展前景。
以面向对象编程方法为基础，采用Visual C++6.0(VC++)为开发工具，在Windows环境下开发了浮选泡沫图像识别系统。整个系统主要由分布查看、点运算、边缘检测、轮廓提取、图像运算、物理参数及识别结果等几大模块组成，采用可扩展及开放的代码。通过继承VC++的基类以及重载相应的虚函数，避开大图像内存分配不足的缺点。
利用该识别系统分析从湘安钨业公司选厂采集来的浮选泡沫图像及其精矿显微图像，提取这些图像的纹理参数(熵、能量、矩、灰度均值)。采用MATLAB工具，建立图像纹理参数与精矿品位之间的数学模型。通过回归分析得出，图像纹理参数与精矿品位存在一定的线性关系。利用所建立的数学模型对精矿品位高于5％的泡沫图像进行品位检测，得到的预测品位与实测品位的相对误差均不大于1.45％，平均相对误差为0.648％。利用所建立的数学模型对精矿显微图像进行品位检测时发现，精矿显微图像的识别精度比浮选泡沫图像识别的精度高。分析预测品位与实测品位产生误差的原因，并找出精矿显微图像识别比浮选泡沫图像识别效果好的原因。
通过浮选泡沫、精矿显微图像识别证实，浮选泡沫图像识别系统可以初步实现选厂浮选泡沫精矿品位的在线与离线检测。
The development and application of digital image technology have been summarized in this dissertation. The application and algorithmic research of foreign and domestic digital image processing in flotation operation have been introduced and predicted.
The Flotation Froth Image Recognition System (FFIRS) has been exploited under Microsoft Windows environment, which is based on the object-oriented programming method and the tool of Visual C++ 6.0 language. The software system mainly consists of distributed check, point operation, orthogonality transform, edge detection, lineament extraction, image operation, physical parameter, recognition result. An extendible and open code is adapted to the system. The limitation of conventional memory for large image is broken through by inheriting base classes of Visual C++ and overriding relevant virtual functions.
The images of flotation forth and extractive mineral obtained from Tungsten Flotation Factory in XiangAn have been analyzed, and the texture parameters (entropy, energy, moments and average of gray level) have been distilled. We have established mathematic models between the texture parameters in the image and the grade of concentrated mineral in flotation forth with MATLAB language. There exists linear relation between the texture parameters and grade of concentrated mineral by using regression analysis.
Detecting the flotation froth images of concentrated mineral whose grade is higher than 5 percent by using established mathematic model, the relative error between forecasted grade and original grade is no higher than 1.45 percent and the average relative error is 0.648 percent. Detecting microphotograph of concentrated mineral by using established mathematic model, you will find that the effect of recognition of microphotograph in-concentrated mineral is better than the effect of recognition of flotation froth. The reason why there is an error between forecasted grade and original grade has been analyzed. The reason why the effect of recognition of microphotograph in concentrated mineral is better than the effect of recognition of flotation froth has been found.
FFIRS can elementarily detect grade of concentrated mineral online or outline. It has been proved by using image recognition of flotation froth and image recognition of microphotograph in concentrated mineral.
以面向对象编程方法为基础，采用Visual C++6.0(VC++)为开发工具，在Windows环境下开发了浮选泡沫图像识别系统。整个系统主要由分布查看、点运算、边缘检测、轮廓提取、图像运算、物理参数及识别结果等几大模块组成，采用可扩展及开放的代码。通过继承VC++的基类以及重载相应的虚函数，避开大图像内存分配不足的缺点。
利用该识别系统分析从湘安钨业公司选厂采集来的浮选泡沫图像及其精矿显微图像，提取这些图像的纹理参数(熵、能量、矩、灰度均值)。采用MATLAB工具，建立图像纹理参数与精矿品位之间的数学模型。通过回归分析得出，图像纹理参数与精矿品位存在一定的线性关系。利用所建立的数学模型对精矿品位高于5％的泡沫图像进行品位检测，得到的预测品位与实测品位的相对误差均不大于1.45％，平均相对误差为0.648％。利用所建立的数学模型对精矿显微图像进行品位检测时发现，精矿显微图像的识别精度比浮选泡沫图像识别的精度高。分析预测品位与实测品位产生误差的原因，并找出精矿显微图像识别比浮选泡沫图像识别效果好的原因。
通过浮选泡沫、精矿显微图像识别证实，浮选泡沫图像识别系统可以初步实现选厂浮选泡沫精矿品位的在线与离线检测。
The development and application of digital image technology have been summarized in this dissertation. The application and algorithmic research of foreign and domestic digital image processing in flotation operation have been introduced and predicted.
The Flotation Froth Image Recognition System (FFIRS) has been exploited under Microsoft Windows environment, which is based on the object-oriented programming method and the tool of Visual C++ 6.0 language. The software system mainly consists of distributed check, point operation, orthogonality transform, edge detection, lineament extraction, image operation, physical parameter, recognition result. An extendible and open code is adapted to the system. The limitation of conventional memory for large image is broken through by inheriting base classes of Visual C++ and overriding relevant virtual functions.
The images of flotation forth and extractive mineral obtained from Tungsten Flotation Factory in XiangAn have been analyzed, and the texture parameters (entropy, energy, moments and average of gray level) have been distilled. We have established mathematic models between the texture parameters in the image and the grade of concentrated mineral in flotation forth with MATLAB language. There exists linear relation between the texture parameters and grade of concentrated mineral by using regression analysis.
Detecting the flotation froth images of concentrated mineral whose grade is higher than 5 percent by using established mathematic model, the relative error between forecasted grade and original grade is no higher than 1.45 percent and the average relative error is 0.648 percent. Detecting microphotograph of concentrated mineral by using established mathematic model, you will find that the effect of recognition of microphotograph in-concentrated mineral is better than the effect of recognition of flotation froth. The reason why there is an error between forecasted grade and original grade has been analyzed. The reason why the effect of recognition of microphotograph in concentrated mineral is better than the effect of recognition of flotation froth has been found.
FFIRS can elementarily detect grade of concentrated mineral online or outline. It has been proved by using image recognition of flotation froth and image recognition of microphotograph in concentrated mineral.
引文
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