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Evaluation of Knowledge Gaps in Mathematical Applications of Thermal Image Processing Techniques for Fire Prevention

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Published:06 March 2017Publication History
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Abstract

In this article, we present literature reviews on fire prevention methods, especially in mining industries, using thermal image processing techniques. Fire protection systems are crucial because of the increased loss of human lives due to coal fires and fatal explosions in coal mines across the world in the past few decades. And with the growth in the demand for energy and the mining of coal expected up to the year 2050, determining conditions leading up to a breakout of fire is paramount. To detect uncertain fire breakout conditions, thermal imaging is considered the most significant among several early warning methods to recognize spontaneous combustion of coal piles (e.g., temperature recordings by sensors, compaction testing of ore seam, gas tests). The evolution of thermographic imaging applied in various industrial sectors (e.g., coal furnaces, oil tankers, building inspections, security) with numerous applications of mathematical models will be presented in the light of safety dimensions in the mining industry. The missing links or unattended areas of mathematics in the application of thermal image processing in mining, especially in the coal industry, will be evolved as the gap in knowledge suggested in our concluding statements.

References

  1. Rafeef Abu-Gharbieh, Clemens Kaminski, Tomas Gustavsson, and Ghassan Hamarneh. 2001. Flame front matching and tracking in PLIF images using geodesic paths and level sets. In Proceedings of the IEEE Workshop on Variational and Level Set Methods in Computer Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. André Aichert. 2008. Feature extraction techniques. In Proceedings of the Camp Medical Seminar. 325--333.Google ScholarGoogle Scholar
  3. Sigurd Angenent, Eric Pichon, and Allen Tannenbaum. 2006. Mathematical methods in medical image processing. Bulletin of the American Mathematical Society 43, 365--396. Google ScholarGoogle ScholarCross RefCross Ref
  4. Bodil Anjar, Mats Dalberg, and Magnus Uppsäll. 2011. Feasibility study of thermal condition monitoring and condition based maintenance in wind turbines. Elforsk Rapport 11, 19.Google ScholarGoogle Scholar
  5. Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Mark J. Carlotto. 2007. Feature-based anomaly detection. Proceedings of the SPIE 6567, Article No. 65671A.Google ScholarGoogle Scholar
  7. Turgay Celik. 2010. Fast and efficient method for fire detection using image processing. ETRI Journal 32, 6, 881--890. Google ScholarGoogle ScholarCross RefCross Ref
  8. Tony F. Chan and Luminita A. Vese. 2001. Active contours without edges. IEEE Transactions on Image Processing 10, 2, 266--277. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sotirios P. Chatzis and Theodora A. Varvarigou. 2008. A fuzzy clustering approach toward hidden Markov random field models for enhanced spatially constrained image segmentation. IEEE Transactions on Fuzzy Systems 16, 5, 1351--1361. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ding De-Hong, Fang Kui, Yu He-XSSiang, Qian Ke-Jun, Chen Yi-Neng, and Cui Dai-Jun. 2014. Multi-emissivity setting in thermal imaging based on visible-light image segmentation. Telkomnika Indonesian Journal of Electrical Engineering 12, 1, 245--253Google ScholarGoogle ScholarCross RefCross Ref
  11. K. S. R. Dubba, A. G. Cohn, and D. C. Hogg. 2010. Event model learning from complex videos using ILP. In Proceedings of the 9th European Conference on Artificial Intelligence. 16--20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Etehad Tavakol, S. Sadri, and E. Y. K. Ng. 2010. Application of K- and fuzzy c-means for color segmentation of thermal infrared breast images. Journal of Medical Systems 34, 1, 35--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Feynman, R. Leighton, and M. Sands. 1964. The Feynman Lectures on Physics. Addison-Wesley.Google ScholarGoogle Scholar
  14. Thomas H. Fletcher, Larry L. Baxter, and David K. Ottesen. 2012. Spectral Emission Characteristics of Size-Graded Coal Particles. Technical Report. Sandia National Laboratories, Albuquerque, NM.Google ScholarGoogle Scholar
  15. David Halliday and Robert Resnick. 2014. Fundamentals of Physics (10th ed.). John Wiley 8 Sons.Google ScholarGoogle Scholar
  16. J. Hernández-Andrés, R. L. Lee Jr., and J. Romero. 1999. Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Applied Optics 38, 5703--5709. Google ScholarGoogle ScholarCross RefCross Ref
  17. Chao-Ching Ho. 2009. Machine vision-based real-time early flame and smoke detection. Measurement Science and Technology 20, 4. Google ScholarGoogle ScholarCross RefCross Ref
  18. Mohd Shawal Jadin, Soib Taib, Shahid Kabir, and Mohd Ansor Bin Yusof. 2011. Image processing methods for evaluating infrared thermographic image of electrical equipments. In Proceedings of the Progress in Electromagnetics Research Symposium.Google ScholarGoogle Scholar
  19. Raymond C. Johnson. 2013. Filtering and Processing Thermal Imagery. Technical Report. College of Earth, Ocean, and Atmospheric Sciences, Corvallis, OR, 1--8.Google ScholarGoogle Scholar
  20. Qasima Abbas Kazmi, Krishna Kant Agrawal, and Vimal Upadhyay. 2013. Image enhancement processing using anisotropic diffusion. International Journal of Computer Science Engineering and Information Technology Research 3, 1, 293--300.Google ScholarGoogle Scholar
  21. Santhana Krishnamachari and Rama Chellappa. 1997. Multiresolution Gauss-Markov random field models for texture segmentation. IEEE Transactions on Image Processing 6, 2, 251--267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. MathWorks. R2013a. Edge detection based segmentation. In MATLAB Manual of Image Processing (3rd ed.).Google ScholarGoogle Scholar
  23. MikroView. 2017. MicroView 2.9. Retrieved January 16, 2017, from http://mikroview.software.informer.com/2.9/.Google ScholarGoogle Scholar
  24. Rob Miller and Murray Shanahan. 2002. Some alternative formulations of the event calculus. In Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II. Springer-Verlag, London, UK, 452--490. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. C. Mythili and V. Kavitha. 2011. Efficient technique for color image noise reduction. Research Bulletin of Jordan, II, 41--44.Google ScholarGoogle Scholar
  26. Newsancai. 2010. Open source image of thermography. Retrieved January 16, 2017, from http://www.newsancai.com/images/stories/newphoto/2010/World/OverseaEducation/ae65.jpg.Google ScholarGoogle Scholar
  27. Yulianto S. Nugroho, Andrew C. McIntosh, and Bernard M. Gibbs. 2000. On the prediction of thermal runaway of coal piles of differing dimension by using a correlation between heat release and activation energy. Proceedings of the Combustion Institute 28, 2, 2321--2327. Google ScholarGoogle ScholarCross RefCross Ref
  28. Giuseppe Papari and Nicolai Petkov. 2011. Edge and line oriented contour detection: State of the art. Image and Vision Computing 29, 79--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. John C. Platt. 1999. Probabilistic output of support vector machine and comparison to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, 61--74.Google ScholarGoogle Scholar
  30. Ramana L. Rao. 1995. Multi Resolution Techniques in Image Processing. Ph.D. Dissertation. Department of Computer Science, Louisiana State University. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Salvador Rego-Barcena, Rebecca Saari, Reza Mani, Sameh El-Batroukh, and Murray J. Thomson. 2007. Real time, non-intrusive measurement of particle emissivity and gas temperature in coal-fired power plants. Measurement Science and Technology 18, 3479--3488. Google ScholarGoogle ScholarCross RefCross Ref
  32. Hui Ren, Wei Jiang, Chaohui Lü, and Shilei Bai. 2009. Monitoring and forecast method of mine external fire. In Proceedings of the International Joint Conference on Computational Sciences and Optimization. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Zahra Shahvaran, Kamran Kazemi, Mohammad Sadegh Helfroush, and Nassim Jafarian. 2012. Region-based active contour model based on Markov random field to segment images with intensity non-uniformity and noise. Journal of Medical Signals and Sensors 2, 1, 17--24.Google ScholarGoogle ScholarCross RefCross Ref
  34. R. Steiner, D. Newell, and E. Williams. 2005. Details of the 1998 watt balance experiment determining the Planck constant. Journal of Research 110, 1, 1--26. Google ScholarGoogle ScholarCross RefCross Ref
  35. Genyun Sun, Xikui Sun, and Xujun Han. 2011. A new method for edge detection based on the criterion of separability. Journal of Multimedia 6, 1, 66--73. Google ScholarGoogle ScholarCross RefCross Ref
  36. Quang Tung Thieu, Marie Luong, Jean-Marie Rocchisani, Nguyen Linh-Trung, and Emmanuel Viennet. 2012. Novel active contour model for image segmentation based on local fuzzy Gaussian distribution fitting. Journal of Electronic Science and Technology 10, 2, 113--118.Google ScholarGoogle Scholar
  37. U.S. Department of Energy. 2014. Windows and Building Envelope Research and Development: Roadmap for Emerging Technologies. U.S. Department of Energy, Washington, DC.Google ScholarGoogle Scholar
  38. R. Vardasca and J. Gabriel. 2014. A proposal of a standard rainbow false color scale for thermal medical images. In Proceedings of the 12th Quantitative InfraRed Thermography Conference (QIRT’14). Google ScholarGoogle ScholarCross RefCross Ref
  39. B. Wah (Ed.). 2009. Wiley Encyclopedia of Computer Graphics and Engineering. John Wiley 8 Sons. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. K. Weeratunga and C. Kamath. 2004. An investigation of implicit active contours for scientific image segmentation. In Proceedings of the Visual Communications and Image Processing Conference. 1--12. Google ScholarGoogle ScholarCross RefCross Ref
  41. Zhuang Wei, Chen Yunhao, Cai Hongchun, and Xu Jie. 2007. Extracting thermal anomaly point of underground coal fire from multi-temporal daytime images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS’07).Google ScholarGoogle Scholar
  42. Yuan Wen-Ju, Xu Kun, and Hou A. Lin. 2010. Reconstruction of 3-D temperature distribution for combustion flame image. In Proceedings of the IEEE International Conference on Computer, Mechatronics, Control, and Electronic Engineering (CMCE’10). IEEE, Los Alamitos, CA. Google ScholarGoogle ScholarCross RefCross Ref
  43. M. White. 1999. Anisotropies in the CMB, DPF 99. In Proceedings of the Los Angeles Meeting, 1999 Meeting of the Division of Particles and Fields, of the American Physical Society. USA.Google ScholarGoogle Scholar
  44. B. Wiecek, C. Peszynski-Drews, M. Wysocki, T. Jakubowska, R. Danych, and S. Zwolenik. 2003. Advanced Methods of Thermal Image Processing for Medical and Biological Applications. Retrieved January 16, 2017, from http://www.dtic.mil/dtic/tr/fulltext/u2/a409962.pdf.Google ScholarGoogle Scholar
  45. Min Xie, Jianhua Wu, Lin Zhang, and Chun Li. 2009. A novel boiler flame image segmentation and tracking algorithm based on YCbCr color space. In Proceedings of the IEEE International Conference on Information and Automation.Google ScholarGoogle Scholar
  46. Ian T. Young, Jan J. Gerbrands, and Lucas J. van Vliet. 2004. Fundamentals of Image Processing. Version 2.3. Delft University of Technology, Delft, Netherlands.Google ScholarGoogle Scholar
  47. Miao Yu, Adel A. Rhuma, Syed Mohsen Naqvi, and Liang Wang. 2012. Posture recognition based fall detection system for monitoring an elderly person in a smart home environment. IEEE Transactions on Information Technology in Biomedicine 16, 6, 1274--1286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Wang Yuanbin and Ma Xianmin. 2010. Research on fire detection in coalmine based on fuzzy neural network. In Proceedings of the 6th International Conference on Natural Computation (ICNC’10).Google ScholarGoogle Scholar
  49. Wang Yun-Jia, Sheng Yao-Bin, Gu Qiang, Sun Yue-Yue, Wei Xiu-Jun, and Zhang Zhi-Jie. 2008. Infrared Thermography Monitoring and Early WaSrning of the Spontaneous Combustion of Coal Gangue Pile. Retrieved January 16, 2017, from http://www.isprs.org/proceedings/XXXVII/congress/8_pdf/2_WG-VIII-2/08a.pdf.Google ScholarGoogle Scholar
  50. Xin Zhang, Chenggang Zhen, Pu Han, and Fang Gao. 2006. Extraction of characteristic parameters of furnace flame based on Markov model. In Proceedings of the 2006 IEEE Symposium on Industrial Electronics. Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 50, Issue 1
      January 2018
      588 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/3058791
      • Editor:
      • Sartaj Sahni
      Issue’s Table of Contents

      Copyright © 2017 ACM

      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 6 March 2017
      • Accepted: 1 October 2016
      • Revised: 1 June 2016
      • Received: 1 January 2016
      Published in csur Volume 50, Issue 1

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