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      • Department of Electrical and Electronics Engineering
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      Cepstrum based feature extraction method for fungus detection

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      Author
      Yorulmaz, Onur
      Pearson, T.C.
      Çetin, A. Enis
      Date
      2011
      Source Title
      Proceedings of SPIE
      Print ISSN
      0277-786X
      Publisher
      SPIE
      Volume
      8027
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
      Keywords
      Cepstrum Analysis
      Fungus Detection in Popcorn Kernels
      Image Processing
      SVM
      Cepstral features
      Cepstrum
      Cepstrum analysis
      Feature extraction methods
      Kernel image
      Recognition rates
      SVM
      Agriculture
      Feature extraction
      Food safety
      Imaging systems
      Support vector machines
      Image processing
      Permalink
      http://hdl.handle.net/11693/28354
      Published Version (Please cite this version)
      http://dx.doi.org/10.1117/12.882406
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      • Department of Electrical and Electronics Engineering 3529
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