结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割

廖一鹏; 王卫星

doi:10.3788/OPE.20162410.2589

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结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割

信息科学 | 更新时间：2020-07-07

- 结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割
- Flotation froth image segmentation based on multiscale edge enhancement and adaptive valley detection
- 光学精密工程 2016年24卷第10期页码：2589-2600
- 作者机构：
  
  福州大学物理与信息工程学院, 福建福州 350108
- 作者简介：
  
  廖一鹏(1982-),男,福建泉州人,博士研究生,讲师,2005年、2008年于福州大学分别获得学士、硕士学位,主要从事图像处理与模式识别方面的研究。E-mail:fzu_lyp@163.com
  [ "王卫星(1959-),男,湖南邵阳人,博士,教授,博士生导师,1982年和1985年分别在国内获得学士学位和工程硕士学位,1997年于瑞典皇家工学院获得博士学位,主要从事图像处理与模式识别、机器视觉应用等方面的研究。E-mail:znn525d@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61170147;61471124)
- DOI：10.3788/OPE.20162410.2589
  中图分类号： TP391.4
- 收稿日期：2016-05-13，
  
  录用日期：2016-6-22，
  
  纸质出版日期：2016-10
- 稿件说明：
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廖一鹏, 王卫星. 结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割[J]. 光学精密工程, 2016,24(10):2589-2600.

Yi-peng LIAO, Wei-xing WANG. Flotation froth image segmentation based on multiscale edge enhancement and adaptive valley detection[J]. Optics and precision engineering, 2016, 24(10): 2589-2600.
廖一鹏, 王卫星. 结合多尺度边缘增强及自适应谷底检测的浮选气泡图像分割[J]. 光学精密工程, 2016,24(10):2589-2600. DOI： 10.3788/OPE.20162410.2589.

Yi-peng LIAO, Wei-xing WANG. Flotation froth image segmentation based on multiscale edge enhancement and adaptive valley detection[J]. Optics and precision engineering, 2016, 24(10): 2589-2600. DOI： 10.3788/OPE.20162410.2589.

摘要

针对浮选气泡图像噪声大、边界弱、传统谷底检测算法对不同类型气泡分割不具普遍性等问题，提出了一种结合Contourlet多尺度边缘增强及自适应谷底边界检测的气泡分割方法。该方法通过对气泡图像进行Contourlet分解，得到多尺度多方向高频子带；通过对各方向子带的高频系数进行非线性增益处理，实现边缘增强和噪声抑制。对和声搜索算法的“调音”策略和参数设定方法进行了改进，对不同类型气泡图像自适应地获取谷底边界检测算法的最优参数，提取谷底并进行形态学的边缘完善处理。最后进行了分割实验，并与其它方法做了比较。结果表明，采用该方法对不同类型气泡进行分割时，平均检测效率（DER）和准确率（ACR）分别为91.2%和90.6%，较传统分割方法有较大提高。该方法无需手工调节参数，自适应能力强，精度高。

Abstract

To overcome the weak edges and large noise of flotation froth image

and to solve the weakness of traditional valley detection algorithm on different kinds of bubble segmentation sizes

a froth image segmentation method was proposed based on Contourlet transform multi-scale edge enhancement and adaptive valley detection. Firstly

the froth image was decomposed by using the Contourlet transfom to obtain multi-scale and multi-direction sub-band coefficients. Then

thresholds of the nonlinear enhancement function were determined according to the coefficients of each scale to enhance edges and suppress the noise. Furthermore

the optimal position adjustment strategy and parameter setting of HS were improved to find the optimal parameters of valley detection algorithm and to detect the different kinds edges of bubble image size. Finally

segmentation experiment was performed and obtained result was further improved by morphological processing. Experiments show that the proposed method effectively detects the edges of different type of bubbles adaptively

and the average detection efficiency (DER) is 91.2% and the average accuracy (ACR) is 90.6%

which is much better than that of traditional methods. This method has high precision

good adaptive ability

and does not need to adjust parameters manually.

关键词

Keywords

references

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