期刊文献+

基于离散余弦变换特征和隐马尔科夫模型的铜熔炼过程烟雾分级 被引量:1

Smoke Classification in Copper Smelting Process Based on Discrete Cosine Transform Features and Hidden Markov Model
原文传递
导出
摘要 为实现铜熔炼过程除尘风机转速的自动调节,提出了基于图像分析技术的烟雾浓度分级方法。通过采样窗对烟雾图像从上至下进行采样,形成时间序列,对每个采样子图进行离散余弦变换(DCT)特征提取,提取的系数视作该时刻隐马尔科夫模型(HMM)隐含状态产生的的观测值,一幅图像则分割成一个完整的HMM序列。通过对4种工况分别建立HMM,每种工况各用30幅图像训练估计模型参数,再对待测烟雾样本图像进行分类。实验结果表明,采用HMM分类的准确率最高可达95%,优于最小二乘支持向量机(LSSVM)的识别效果。 A smoke concentration grading method based on the image analysis technique is proposed for the automatic speed adjustment of the dust removal fan in the copper smelting process.We obtain a sequence of sub images by using a moving window to slide over the whole smoke image from top to bottom.Then,discrete cosine transform (DCT)is utilized to extract the features of each sub-image and the DCT coefficients are vectorized as the observation data for hidden Markov model (HMM).Thus an image is divided into an observed sequence to build the HMM model for grade classification.Four different running states are considered in the smelting process,in which a HMM model is built for each running state.For each running state,30 images are used for the training of HMM model.The results show that the classification accuracy can reach 95%with HMM,which is higher than that of least squares support vector machine (LSSVM).
作者 张宏伟 张凌婕 袁小锋 宋执环 Zhang Hongwei;Zhang Lingjie;Yuan Xiaofeng;Song Zhihuan(College of Electronics and Information,Xi'an Polytechnic University,Xi'an,Shaanxi 710048,China;School of Information Science and Engineering,Central South University,Changsha,Hunan 410083,China;Department of Control Science and Engineering,Zhejiang University,Hangzhou,Zhejiang 310027,China)
出处 《激光与光电子学进展》 CSCD 北大核心 2018年第12期393-401,共9页 Laser & Optoelectronics Progress
基金 国家自然科学基金面上项目(61573308) 陕西省科技厅自然基金(2014JQ-5026).
关键词 图像处理 铜熔炼 图像分析 离散余弦变换(DCT) 隐马尔科夫模型(HMM) 烟雾分级 image processing copper smelting process image analysis discrete cosine transform (DCT) hidden Markov model (HMM) smoke classification
  • 相关文献

参考文献4

二级参考文献39

  • 1L Sirovich,M Kirby. Appfication of Karhunen-Loeve procedure for the characterization of human faces[ J ]. IEEE Trans Pattern Analysis and Machine Intelligence, 1990,3( 1 ) :71 - 79. 被引量:1
  • 2M Turk, A Pentland. Eigenfaces for recognition[ J]. Journal of Cognitive Neuroscience, 1991,3( 1 ) : 72 - 86. 被引量:1
  • 3D L Swets, J Y Weng. Using discriminant eigenfeatures for image retdeval[ J ]. IEEE Trans Pattern Analysis and Machine Intelligence, 1996,18(8) : 831 - 836. 被引量:1
  • 4P N Belhumeur, J P Hespanha, D J Kriegman. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection[ J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1997,19 (7) :711 - 720. 被引量:1
  • 5Z M Hafed, M D Levine. Face recognition using the discrete cosine transform[ J ].International Journal of Computer Vision, 2001,43(3) : 167 - 188. 被引量:1
  • 6D Ramasubramanian, Y V Venkatesh. Encoding and recognition of faces based on the human visual model and DCT[ J]. Pattern Recognition, 2001,34(12) :2447 - 2458. 被引量:1
  • 7W Chen, J E Meng, S Wu. PCA and LDA in DCT domain [ J]. Pattern Recognition Letters,2005,26(15) :2474 - 2482. 被引量:1
  • 8周学成,罗锡文,刘正敏.植物根系原位CT图像分割方法的研究进展[J].计算机工程与设计,2007,28(17):4252-4256. 被引量:4
  • 9丁南南,刘艳滢,张叶,陈春宁,贺柏根.基于SURF-DAISY算法和随机kd树的快速图像配准[J].光电子.激光,2012,23(7):1395-1402. 被引量:40
  • 10卢永芳,卢珂,侯翔文.基于IHS变换的图像融合方法研究[J].科技通报,2012,28(6):212-214. 被引量:4

共引文献53

同被引文献2

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部