Cepstrum based feature extraction method for fungus detection
Author
Yorulmaz, Onur
Pearson, T.C.
Çetin, A. Enis
Date
2011Source Title
Proceedings of SPIE
Print ISSN
0277-786X
Publisher
SPIE
Volume
8027
Language
English
Type
Conference PaperItem Usage Stats
143
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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 AnalysisFungus 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