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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

Smoke Classification in Copper Smelting Process Based on Discrete Cosine Transform Features and Hidden Markov Model

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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。

Abstract

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).

Newport宣传-MKS新实验室计划
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中图分类号:TB8;TF3

DOI:10.3788/lop55.121504

所属栏目:机器视觉

基金项目: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)

收稿日期:2018-04-20

修改稿日期:2018-05-25

网络出版日期:2018-07-12

作者单位    点击查看

张宏伟:西安工程大学电子信息学院, 陕西 西安 710048浙江大学控制科学与工程学院, 浙江 杭州 310027
张凌婕:西安工程大学电子信息学院, 陕西 西安 710048
袁小锋:中南大学信息科学与工程学院, 湖南 长沙 410083
宋执环:浙江大学控制科学与工程学院, 浙江 杭州 310027

联系人作者:张宏伟(zhanghongwei@zju.edu.cn)

【1】Zhang H, Ge Z, Ye L, et al. Vision-based fan speed control system in the copper scraps smelting process[J]. Asian Journal of Control, 2014, 17(5): 1742-1755.

【2】Russ J C. Image analysis of foods[J]. Journal of Food Science, 2015, 80(9): E1974-E1987.

【3】Qin J W, Chao K L, Kim M S, et al. Hyperspectral and multispectral imaging for evaluating food safety and quality[J]. Journal of Food Engineering, 2013, 118(2): 157-171.

【4】Jing J F, Liu S M, Li P F, et al. The fabric defect detection based on CIE L*a*b* color space using 2-D Gabor filter[J]. The Journal of the Textile Institute, 2016, 107(10): 1305-1313.

【5】Schneider D, Holtermann T, Merhof D. A traverse inspection system for high precision visual on-loom fabric defect detection[J]. Machine Vision and Applications, 2014, 25(6): 1585-1599.

【6】Lei N, Soshi M. Vision-based system for chatter identification and process optimization in high-speed milling[J]. International Journal of Advanced Manufacturing Technology, 2017, 89: 2757-2769.

【7】Li X Q, Wang L H, Cai N X. Machine-vision-based surface finish inspection for cutting tool replacement in production[J]. International Journal of Production Research, 2004, 42(11): 2279-2287.

【8】Zhang H W, Ge Z Q, Yuan X F, et al. Rapid vision-based system for secondary copper content estimation[J]. Transactions of Nonferrous Metals Society of China, 2014, 24(8): 2665-2676.

【9】Huang H, Hu X T, Zhao Y, et al. Modeling task fMRI data via deep convolutional autoencoder[J]. IEEE Transactions on Medical Imaging, 2018, 37(7): 1551-1561.

【10】Wang S K, Pan J X, Chen P. Adaptive segmentation algorithm for CT image sequence based on structure continuity as prior information[J]. Laser & Optoelectronics Progress, 2016, 53(11): 111006.
王苏恺, 潘晋孝, 陈平. 基于结构连续先验的CT图像序列自适应分割算法[J]. 激光与光电子学进展, 2016, 53(11): 111006.

【11】Blaschke T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(1): 2-16.

【12】Lorente D, Aleixos N, Gómez-Sanchis J, et al. Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment[J]. Food and Bioprocess Technology, 2012, 5(4): 1121-1142.

【13】Zhu B F, Chen W J, Li W S. Liquid crystal display defect detection based on Fourier-Mellin transform[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121502.
朱炳斐, 陈文建, 李武森. 基于Fourier-Mellin变换的液晶显示屏显示缺陷检测[J]. 激光与光电子学进展, 2017, 54(12): 121502.

【14】Kim C W, Kim H G, Suk H G. A study on the composition determination of Cu alloys by image processing technology[J]. Solid State Phenomena, 2006, 116/117: 795-798.

【15】Appana D K, Islam R, Khan S A, et al. A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems[J]. Information Sciences, 2017, 418: 91-101.

【16】Mredhula L, Dorairangaswamy M A. Image denoising using principal component analysis (PCA) and pixel surge model (PSM)[J]. International Journal of Signal and Imaging Systems Engineering, 2016, 9(4/5): 311-319.

【17】Naidu V P S, Raol J R. Pixel-level image fusion using wavelets and principal component analysis[J]. Defence Science Journal, 2008, 58(3): 338-352.

【18】Hanbay K, Talu M F, Ozguven O F. Real time fabric defect detection by using Fourier transform[J]. Journal of the Faculty of Engineering and Architecture of Gazi University, 2017, 32(1): 151-158.

【19】Lin Z C, He J F, Tang X O, et al. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis[J]. Pattern Recognition, 2009, 42(11): 2492-2501.

【20】Hernandez J R, Amado M, Perez-Gonzalez F. DCT-domain watermarking techniques for still images: detector performance analysis and a new structure[J]. IEEE Transactions on Image Processing, 2000, 9(1): 55-68.

【21】Yin H T, Fu P, Sha X J. Face recognition based on DCT and LDA[J]. Acta Electronica Sinica, 2009, 37(10): 2211-2214.
尹洪涛, 付平, 沙学军. 基于DCT和线性判别分析的人脸识别[J]. 电子学报, 2009, 37(10): 2211-2214.

【22】Bicego M, Murino V, FigueiredoM A T. A sequential pruning strategy for the selection of the number of states in hidden Markov models[J]. Pattern Recognition Letters, 2003, 24: 1395-1407.

【23】Lu B, Gu S H. Object tracking algorithm based on hidden Markov model and block feature matching[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091006.
陆兵, 顾苏杭. 基于隐马尔可夫模型和分块特征匹配的目标跟踪算法[J]. 激光与光电子学进展, 2017, 54(9): 091006.

引用该论文

Zhang Hongwei,Zhang Lingjie,Yuan Xiaofeng,Song Zhihuan. Smoke Classification in Copper Smelting Process Based on Discrete Cosine Transform Features and Hidden Markov Model[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121504

张宏伟,张凌婕,袁小锋,宋执环. 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[J]. 激光与光电子学进展, 2018, 55(12): 121504

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