A Dynamic Flame Image Segmentation Method and Its Application in Video Monitoring of Fused Magnesium Furnace Process

doi:10.12068/j.issn.1005-3026.2019.02.001

Abstract

Abstract: The color， brightness distribution and shape pattern of flames vary under different working conditions， thus showing multi-modality. In addition， the background environment is highly coupled with the foreground image because of the reflection， aerosol， dust and other interferences. Traditional segmentation algorithms cannot ensure accuracy under a variety of conditions. A method of flame image segmentation based on multivariate image analysis and expert knowledge is proposed. The principal component analysis method was used to reduce the dimension of images and to construct the scatter plot of the scores， so as to obtain the regions of flame segmentation. The method was applied to the video monitoring of the production of fused magnesium furnace， which verified the performance of this method.

Key words: image segmentation, dynamic flame, multivariate image analysis, fused magnesium furnace process, video monitoring

CLC Number:

TP39

LU Shao-wen， LI Peng-qi， ZHENG Xiu-ping， GUO Zhang. A Dynamic Flame Image Segmentation Method and Its Application in Video Monitoring of Fused Magnesium Furnace Process[J]. Journal of Northeastern University Natural Science, 2019, 40(2): 153-158.

References

[1]David C，Blake C R，Thomas F E.Multivariate image analysis for industrial flare combustion control［J］.Industrial & Engineering Chemistry Research，2012，51(39):12642-12652.
[2]Lu S W，Yue H.Real-time data driven visual simulation of process manufacturing:a case study［C］//2011 Chinese Control and Decision Conference(CCDC).Mianyang，China，2011:1806-1809.
[3]Petrou M，Sevilla P G.Image processing dealing with texture［M］.Chichester，UK:Wiley，2006.
[4]Resmi M R，Vishnukumar S.A novel segmentation based copy-move forgery detection in digital images ［C］//International Conference on Networks & Advances in Computational Technologies(NetACT).Thiruvanthapuram，India，2017:346-350.
[5]Gao R，Liu X L.Research and implementation of digital video matting technology research the matting technology algorithm ［C］// International Conference on Electronics，Communications and Control(ICECC).Ningbo，China，2011:2229-2232.
[6]Li W T，Wang D H，Chai T Y.Flame image-based burning state recognition for sintering process of rotary kiln using heterogeneous features and fuzzy integral ［J］.IEEE Transactions on Industrial Informatics，2012，8(4):780-790.
[7]Zhu Q S，Shao L，Li X L，et al.Targeting accurate object extraction from an image:a comprehensive study of natural image matting ［J］. IEEE Transactions on Neural Networks and Learning Systems，2015，25(2):185-207.
[8]王培珍，杨维翰.图像分割的分层处理方案［J］.安徽工业大学学报，2002，19(3):205-208.(Wang Pei-zhen，Yang Wei-han.Image segmentation of the hierarchical approach［J］.Journal of Anhui University of Technology，2002，19(3):205-208.)
[9]Otsu N.A threshold selection method from gray-level histograms ［J］. IEEE Transactions on Systems，Man，and Cybernetics，1979，9(1):62-66.
[10]Dunn J C.A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters ［J］.Journal of Cybernetics，1973，3(3):32-57.
[11]Geladi P，Grahn H F.Multivariate image analysis ［M］.Chichester，UK:Wiley，1996.
[12]吴志伟，方正，柴天佑，等.电熔镁炉嵌入式专用控制器及其控制方法研究［J］.仪器仪表学报，2012，33(6):1261-1267.(Wu Zhi-wei，Fang Zheng，Chai Tian-you，et al.Research on embedded special controller of electric melting furnace and its control method［J］. Journal of Instrumentation，2012，33(6):1261-1267.)
[13]Chen Q，Li D，Tang C K.KNN Matting［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2013，35(9):2175-2188.
[14]Lin Y，Lv F，Zhu S，et al.Large-scale image classification:Fast feature extraction and SVM training［C］// 2011 IEEE Conference on Computer Vision and Pattern Recognition.Colorado Springs，USA，2011:1689-1696.

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