基于Otsu图像分割的颗粒与气泡碰撞检测方法
发布时间：2018-07-26 18:25
【摘要】：为了准确地检测出浮选过程中颗粒与气泡是否发生碰撞,提出了一种基于形态学图像处理和最大类间方差法(Otsu)的颗粒与气泡碰撞检测方法。首先利用图像的预处理操作优化图像,通过最大类间方差法分割出目标区域与背景区域,然后结合最小二乘法将颗粒与气泡拟合成圆,方便求取坐标信息及半径,最后将所有的颗粒位置提取到一幅图像中,实现了对颗粒位置的跟踪,避免了连续帧图像跟踪的复杂程度。结果表明,该算法能够快速地检测出颗粒与气泡是否发生碰撞,有效地节省了人工试验的工作时间。
[Abstract]:In order to detect accurately whether particles collide with bubbles in flotation, a collision detection method based on morphological image processing and maximum inter-class variance method (Otsu) is proposed. Firstly, the image is optimized by image preprocessing, and the target area and background area are segmented by the method of maximum inter-class variance, and then the particle and bubble are combined with the least square method to obtain the coordinate information and radius. Finally, all the particle positions are extracted into a single image, and the tracking of the particle positions is realized, which avoids the complexity of continuous frame image tracking. The results show that the algorithm can quickly detect whether the particles collide with the bubble and save the working time of the artificial experiment.
【作者单位】： 华北理工大学电气工程学院;华北理工大学矿业工程学院;
【基金】：国家自然科学基金项目资助(编号:51474087)
【分类号】：TD923;TP391.41
，
本文编号：2146906
[Abstract]:In order to detect accurately whether particles collide with bubbles in flotation, a collision detection method based on morphological image processing and maximum inter-class variance method (Otsu) is proposed. Firstly, the image is optimized by image preprocessing, and the target area and background area are segmented by the method of maximum inter-class variance, and then the particle and bubble are combined with the least square method to obtain the coordinate information and radius. Finally, all the particle positions are extracted into a single image, and the tracking of the particle positions is realized, which avoids the complexity of continuous frame image tracking. The results show that the algorithm can quickly detect whether the particles collide with the bubble and save the working time of the artificial experiment.
【作者单位】： 华北理工大学电气工程学院;华北理工大学矿业工程学院;
【基金】：国家自然科学基金项目资助(编号:51474087)
【分类号】：TD923;TP391.41
，
本文编号：2146906
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