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      • Department of Electrical and Electronics Engineering
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      Compressive sensing based flame detection in infrared videos [Kizilötesi videolarda sikiştirmali algilama ile alev tespiti]

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      Author
      Günay O.
      Enis Çetin, A.
      Date
      2013
      Journal Title
      2013 21st Signal Processing and Communications Applications Conference, SIU 2013
      Language
      Turkish
      Type
      Conference Paper
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      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/27993
      Abstract
      In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
      Published as
      http://dx.doi.org/10.1109/SIU.2013.6531547
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      • Department of Electrical and Electronics Engineering 2964

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