Cepstrum based feature extraction method for fungus detection

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.volumeNumber8027en_US
dc.contributor.authorYorulmaz, Onuren_US
dc.contributor.authorPearson, T.C.en_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialOrlando, Florida, United Statesen_US
dc.date.accessioned2016-02-08T12:18:11Z
dc.date.available2016-02-08T12:18:11Z
dc.date.issued2011en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: Proceedings of SPIE, Sensing for Agriculture and Food Quality and Safety IIIen_US
dc.descriptionDate of Conference: 26–27 April 2011en_US
dc.description.abstractIn 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).en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:18:11Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2011en
dc.identifier.doi10.1117/12.882406en_US
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11693/28354
dc.language.isoEnglishen_US
dc.publisherSPIEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1117/12.882406en_US
dc.source.titleProceedings of SPIEen_US
dc.subjectCepstrum Analysisen_US
dc.subjectFungus Detection in Popcorn Kernelsen_US
dc.subjectImage Processingen_US
dc.subjectSVMen_US
dc.subjectCepstral featuresen_US
dc.subjectCepstrumen_US
dc.subjectCepstrum analysisen_US
dc.subjectFeature extraction methodsen_US
dc.subjectKernel imageen_US
dc.subjectRecognition ratesen_US
dc.subjectSVMen_US
dc.subjectAgricultureen_US
dc.subjectFeature extractionen_US
dc.subjectFood safetyen_US
dc.subjectImaging systemsen_US
dc.subjectSupport vector machinesen_US
dc.subjectImage processingen_US
dc.titleCepstrum based feature extraction method for fungus detectionen_US
dc.typeConference Paperen_US

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