A New Recognition Method for Flotation Froth Images

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A New Recognition Method for Flotation Froth Images

更新时间：2025-07-09

- A New Recognition Method for Flotation Froth Images
- Vol. 45, Issue 4, Pages: 104-108(2011)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TD928.9
- Online First：10 April 2011，
  
  Published：2011
- 稿件说明：
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A New Recognition Method for Flotation Froth Images[J]. 2011, 45(4): 104-108.
DOI：

A New Recognition Method for Flotation Froth Images[J]. 2011, 45(4): 104-108. DOI：

摘要

针对灰度共生矩阵法提取的浮选泡沫图像纹理特征相互混叠

不利于聚类和识别的问题

提出一种基于正交保局投影和支持向量机的浮选泡沫图像识别新方法.该方法利用正交保局投影法对原始纹理特征参数进行变换处理

有效改变了不同类别特征参数的聚集程度

并利用支持向量机进行分类.实验结果表明

所提方法的正确识别率能够达到93.5%

与基于最近邻分类器的主元分析法相比

其性能更好.

Abstract

A novel approach to classify the flotation froth images based on OLPP and support vector machine is presented to deal with the problem that the texture feature vectors of digitized images of the froth obtained from the use of gray-level dependence matrix method can not provide a compact representation of froth properties. The texture feature vectors from OLPP is transformed

and then

classification is investigated by using a multi-class SVM. Results obtained encourage to apply the approach to develop a classification system for flotation froth images. Comparison between OLPP with SVM and PCA with nearest neighbor classifier shows that the proposed recognition technology has higher precision and robustness.

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Keywords

references

BONIFAZIA G, SERRANTIA S, VOLPEB F, et al. Characterisation of flotation froth colour and structure by machine vision[J].Computers and Geosciences, 2001, 27(9):1111-1117.

WOODBUM E T, STOCKTON J B, ROBBINS D J. Vision-based characterization of three-phase froths[C]∥Proceedings of the International Colloquium on Developments in Froth Flotation. Piscataway, NJ, USA: IEEE, 1989: 1-30.

MOOLMAN D W, ALDRICH C, VAN DEVENTER J S J, et al. The classification of froth structures in a copper flotation plant by means of neural networks [J]. Chemical Engineering Science, 1995, 43(3/4):193-208.

SINGH V, RAO S M. Application of image processing and radial basis neural network techniques for ore sorting and ore classification [J].Minerals Engineering, 2005, 18(15): 1412-1420.

WANG W X, LI L. Pattern recognition and computer vision for mineral froth[C]∥Proceedings of the 18th International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2006: 622-625.

ALDRICH C, MARAIS C, SHEAN B J, et al. Online monitoring and control of froth flotation systems with machine vision: a review [J]. International Journal of Mineral Processing, 2010, 96(1/4):1-13.

曾荣,沃国经. 图像处理技术在镍选矿厂中的应用[J]. 矿冶,2002, 11(1):37-41.

ZENG Rong, WO Guojing. Application of the image processing technology in nickel concentrator [J]. Mining and Metallurgy, 2002,11(1): 37-41.

刘文礼,路迈西,王帆,等.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J].化工学报,2003,54(6):830-835.

LIU Wenli, LU Maixi, WANG Fan, et al. Extraction of textural feature and recognition of coal [J]. Journal of Chemical Industry and Engineering, 2003,54(6):830-835.

申清明,高建民,李成.焊缝缺陷类型识别方法的研究[J].西安交通大学学报,2010,44(7): 100-103.

SHEN Qingming, GAO Jianmin, LI Cheng. Recognition of weld defect types [J]. Journal of Xi'an Jiaotong University, 2010,44(7):100-103.

HARALICK R,SHANMUGAN K,DINSTEIN I. Texture feature for image classification [J].IEEE Transactions on Systems, Man and Cybernetics,1973,3(6):610-621.

LEMER B, HUGO G, MAYER A, et al. On pattern classification with Sammon's nonlinear mapping: an experimental study [J]. Pattern Recognition,1998, 31(4): 371-381.

HE Xiaofei, YAN Shuicheng, HU Yuxiao, et al. Face recognition using Laplacianfaces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.

CAI Deng, HE Xiaofei, HAN Jiawei, et al. Orthogonal Laplacianfaces for face recognition [J]. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614.

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