基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割

A Segmentation Method Based on Clustering Pre-segmentation and High-low Scale Distance Reconstruction for Colour Froth Image

该文针对矿物浮选过程泡沫图像质量不理想、气泡大小形状灰度不均的问题,提出一种基于聚类预分割和高低精度距离重构的泡沫图像分割方法。首先,利用k-均值聚类进行前景泡沫与背景矿浆彩色图像分割,依据灰度分布和形状分布特征对提取到的泡沫图像进行滤波;然后,基于形态重构提出结合高低精度距离变换对距离图像进行重构,同时利用面积重构h顶改进变换为分水岭变换提取准确的特征标识;最后利用分水岭算法得到分水线,从而完成浮选泡沫的分割。由分割后的泡沫图像可统计分析出气泡个数与尺寸等物理特征参数从而为浮选控制提供依据。仿真结果表明了方法的有效性。

Due to a large variation in the quality of froth images of ore and inhomogeneity of size, shape and grayscale of bubbles, a new segmentation method based on clustering pre-segmentation and high-low scale distance reconstruction is proposed for froth images. Firstly, the segmentation between foreground froth and background mineral slurry image is achieved by the k-means clustering method and the noises are filtered according to intensity distribution and shape distribution information, and a new reconstruction combined with high-low scale distance transformation based on morphological reconstruction is presented and applied to the froth distancetransformation image. Then the precise region makers for watershed transformation are extracted by area-reconstruction h-dome improved transformation. Finally, the watershed algorithm is used to get waterline for every bubble. Bubble physical characteristics such as the bubble number and bubble size can be obtained from the segmented image,which provide the guidance for flotation control process. The experimental results show its effectiveness.