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      • Department of Electrical and Electronics Engineering
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      Online adaptive decision fusion framework based on projections onto convex sets with application to wildfire detection in video

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      Author(s)
      Gunay, O.
      Toreyin, B. U.
      Çetin, A. Enis
      Date
      2011-07-06
      Source Title
      Optical Engineering
      Print ISSN
      0091-3286
      Publisher
      S P I E - International Society for Optical Engineering
      Volume
      50
      Issue
      7
      Pages
      77202-1 - 77202-12
      Language
      English
      Type
      Article
      Item Usage Stats
      134
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      98
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      Abstract
      In this paper, an online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing orthogonal projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are presented.
      Keywords
      Active learning
      Decision fusion
      Online learning
      Projection onto convex sets
      Wild-fire detection
      Permalink
      http://hdl.handle.net/11693/12282
      Published Version (Please cite this version)
      http://dx.doi.org/10.1117/1.3595426
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      • Department of Electrical and Electronics Engineering 3702
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