一种新的浮选泡沫图像识别方法

郝元宏 1; 韩静 2; 齐春 1

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一种新的浮选泡沫图像识别方法

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- 一种新的浮选泡沫图像识别方法
- A New Recognition Method for Flotation Froth Images
- 西安交通大学学报 2011年45卷第4期页码：104-108
- 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：
  
  : 2010-11-05. 作者简介: 郝元宏(1982-),男,博士生;齐春(联系人),男,教授,博士生导师. 基金项目: 陕西省自然科学基金资助项目(2003E212)
- DOI：
  中图分类号： TD928.9
- 网络首发：2011-04-10，
  
  纸质出版：2011
- 稿件说明：
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郝元宏 1, 韩静 2, 齐春 1. 一种新的浮选泡沫图像识别方法[J]. 西安交通大学学报, 2011,45(4):104-108.

A New Recognition Method for Flotation Froth Images[J]. 2011, 45(4): 104-108.
郝元宏 1, 韩静 2, 齐春 1. 一种新的浮选泡沫图像识别方法[J]. 西安交通大学学报, 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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references

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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.

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