Cepstrum based feature extraction method for fungus detection
Date
2011
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Source Title
Proceedings of SPIE
Print ISSN
0277-786X
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SPIE
Volume
8027
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Language
English
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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).
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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