On-line burning state recognition for sintering process using SSIM index of flame images

Yanjun Lin, Chai Li, Jingxin Zhang, Xiaojie Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


Recognition of burning state based on flame images has been an important issue in sintering process of rotary kiln. Existing methods usually adopt techniques of image segmentation, pattern recognition and machine learning, which have high demands on the quality of samples and computational power. It is challenging to the on-line recognition of the burning state, which is essential in realtime control systems. This paper proposes a new approach to the burning state
recognition by comparing the structural similarity (SSIM) index of flame images. The burning state is identified according to the maximum SSIM index between the real time flame image and images in two standard libraries which consist reference images with normal-burning state and under-burning state respectively. This method has low computational complexity and is suitable for
online control in the rotary kiln system. Simulation results show that the proposed method achieves high recognition accuracy with low computation.
Original languageEnglish
Title of host publication2014 11th World Congress on Intelligent Control and Automation (WCICA)
EditorsHong Wang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2352 - 2357
Number of pages6
ISBN (Print)9781479958269
Publication statusPublished - 2015
EventWorld Congress on Intelligent Control and Automation 2014 - Shenyang, China
Duration: 29 Jun 20144 Jul 2014
Conference number: 11th


ConferenceWorld Congress on Intelligent Control and Automation 2014
Abbreviated titleWCICA 2014


  • On-line burning state recognition
  • structural similarity (SSIM)
  • flame images
  • low-pass filters

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