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Proceedings Paper

Cepstrum based feature extraction method for fungus detection
Author(s): Onur Yorulmaz; Tom C. Pearson; A. Enis Çetin
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Paper 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%.

Paper Details

Date Published: 2 June 2011
PDF: 7 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270E (2 June 2011); doi: 10.1117/12.882406
Show Author Affiliations
Onur Yorulmaz, Bilkent Univ. (Turkey)
Tom C. Pearson, U.S.D.A. Agricultural Research Service (United States)
A. Enis Çetin, Bilkent Univ. (Turkey)

Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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