Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics

Kang LIU; Xiao-lin CHEN; Yan-jun LIU; Hao LIANG

doi:10.3788/YJYXS20183311.0936

您当前的位置：

首页 >

文章列表页 >

Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics

Image Processing | 更新时间：2020-08-12

- Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 11, Pages: 936-942(2018)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183311.0936
  CLC： TP391.41
- Received：29 May 2018，
  
  Accepted：27 July 2018，
  
  Published：05 November 2018
- 稿件说明：
移动端阅览
Kang LIU, Xiao-lin CHEN, Yan-jun LIU, et al. Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 936-942.
DOI：

Kang LIU, Xiao-lin CHEN, Yan-jun LIU, et al. Vane pump assembly quality detection based on gabor and gray level co-occurrence matrix hybrid characteristics[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 936-942. DOI： 10.3788/YJYXS20183311.0936.

摘要

本文提出一种Gabor和灰度共生矩阵相结合的特征来检测叶片泵中叶片装配质量的方法。首先构建叶片图像数据集，用5种尺度的和4种方向的Gabor滤波器对图像滤波，根据滤波后的图像计算得到幅值特征图，然后提取幅值特征图的灰度共生矩阵特征，最后融合归一化各个幅值特征图提取到的特征，利用主成分分析法降维，并用这些特征向量训练支持向量机（SVM）分类器，实现对叶片装配质量的评估。将本文提出的混合特征与LBP特征、灰度共生矩阵分别进行了比较得到的分类效果约提高了约10%。基于Gabor和灰度共生矩阵混合特征的叶片装配质量检测准确率提升到了93%。实验结果表明Gabor特征和灰度共生矩阵结合后能够很好从多尺度、多方向上提取图像的纹理特征，并应用于图像分类取得了良好的效果，在一些图像识别上有很宽广的应用前景。

Abstract

This paper presents a method to detect blade assembly quality in a vane pump by features of the combination of Gabor and gray level co-occurrence matrix. Firstly

the blade image dataset is constructed

and the image is filtered by five-scale and four-direction Gabor filters. The amplitude feature map is calculated on the basis of the filtered image

and then the gray level co-occurrence matrix feature of the amplitude feature map is extracted. Finally

we combine and normalize the features extracted from each feature map

reduce the dimensions by principal component analysis

train the support vector machine (SVM) classifier with these feature vectors to achieve the evaluation of blade assembly quality. Respectively comparing mixing features proposed in this paper with the LBP features and gray level co-occurrence matrix

the classification effect is improved by about 10%. The classification effect of blade assembly quality is improved to 93% based on the combination of Gabor and gray level co-occurrence matrix. The experimental results show that the combination of Gabor features and gray level co-occurrence matrix can extract texture features by multi-scale and multi-directions

and obtain good results by applying to image classification. It has wide application prospects in some image recognition.

关键词

Keywords

references

冯伟兴, 唐墨, 贺波.Visual C++数字图像模式识别技术详解[M].北京:机械工业出版社, 2010.

FENG W X, TANG M, HE B. Digital Image Pattern Recognition Programming Using Visual C++ [M]. Beijing:Machinery Industry Press, 2010. (in Chinese)

王玮, 黄非非, 李见为, 等.使用多尺度LBP特征描述与识别人脸[J].光学精密工程, 2008, 16(4):696-705.

WANG W, HUANG F F, LI J W, et al. Face description and recognition using multi-scale LBP feature[J]. Optics and Precision Engineering, 2008, 16(4):696-705. (in Chinese)

刘丽, 匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报, 2009, 14(4):622-635.

LIU L, KUANG G Y. Overview of image textural feature extraction methods[J]. Journal of Image and Graphics, 2009, 14(4):622-635. (in Chinese)

黄媛媛, 张尤赛.双树复小波域共生矩阵的纹理特征提取方法[J].计算机应用与软件, 2012, 29(7):216-219, 230.

HUANG Y Y, ZHANG Y S. Texture feature extraction methodwith dual-tree complex wavelet domain co-occurrence matrix[J]. Computer Applications and Software, 2012, 29(7):216-219, 230. (in Chinese)

王民, 王静, 王羽笙.一种多尺度灰度共生矩阵的纹理特征提取算法[J].液晶与显示, 2016, 31(10):967-972.

WANG M, WANG J, WANG Y S. Multi-scalealgorithm of texture feature extraction based on gray-level co-occurrence matrix[J]. Chinese Journal of Liquid Crystals and Displays, 2016, 31(10):967-972. (in Chinese)

胡敏, 朱弘, 王晓华, 等.基于梯度Gabor直方图特征的表情识别方法[J].计算机辅助设计与图形学学报, 2013, 25(12):1856-1861.

HU M, ZHU H, WANG X H, et al . Expression recognition method based on gradient Gabor histogram features[J]. Journal of Computer-Aided Design & Computer Graphics , 2013, 25(12):1856-1861. (in Chinese)

肖达, 王润民, 邹孝, 等.基于Gabor变换和灰度梯度共生矩阵的超声无损测温研究[J].传感技术学报, 2017, 30(11):1684-1688.

XIAO D, WANG R M, ZOU X, et al. A noninvasive temperature measurement based on Gabor transform and gray level gradient co-occurrence matrix using ultrasound[J]. Chinese Journal of Sensors and Actuators, 2017, 30(11):1684-1688. (in Chinese)

黄荣娟, 姜佳欣, 唐银凤, 等.基于多特征组合的图像纹理分类[J].计算机应用与软件, 2011, 28(8):12-16, 46.

HUANG R J, JIANG J X, TANG Y F, et al. Feature combination based image texture classification[J]. Computer Applications and Software, 2011, 28(8):12-16, 46. (in Chinese)

宋荣杰, 宁纪锋, 刘秀英, 等.基于纹理特征和SVM的QuickBird影像苹果园提取[J].农业机械学报, 2017, 48(3):188-197.

SONG R J, NING J F, LIU X Y, et al. Apple orchard extraction with QuickBird imagery based on texture features and support vector machine[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(3):188-197. (in Chinese)

孔锐, 张冰. Gabor滤波器参数设置[J].控制与决策, 2012, 27(8):1277-1280.

KONG R, ZHANG B. Design of Gabor filters' parameter[J]. Control and Decision, 2012, 27(8):1277-1280. (in Chinese)

赵川源, 何东健, 乔永亮.基于多光谱图像和数据挖掘的多特征杂草识别方法[J].农业工程学报, 2013, 29(2):192-198.

ZHAO C Y, HE D J, QIAO Y L. Identification method of multi-feature weed based on multi-spectral images and data mining[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(2):192-198. (in Chinese)

耿庆田, 赵浩宇, 于繁华, 等.基于改进HOG特征提取的车型识别算法[J].中国光学, 2018, 11(2):174-181.

GENG Q T, ZHAO H Y, YU F H, et al. Vehicle type recognition algorithm based on improved HOG feature[J] . Chinese Optics, 2018, 11(2):174-181. (in Chinese)

周书仁, 殷建平.基于Haar特性的LBP纹理特征[J].软件学报, 2013, 24(8):1909-1926.

ZHOU S R, YIN J P. LBP texture feature based on Haar characteristics[J]. Journal of Software, 2013, 24(8):1909-1926. (in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Ship detection based on spectral residual and gradient texture fusion features

Related Author

Qing-feng LI

Bin HE

Wen-sheng WANG

Chang SU

Xi-yu HAN

Huai-dan LIANG

Related Institution

School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰