Detection of fungal damaged popcorn using image property covariance features
dc.citation.epage | 52 | en_US |
dc.citation.spage | 47 | en_US |
dc.citation.volumeNumber | 84 | en_US |
dc.contributor.author | Yorulmaz, O. | en_US |
dc.contributor.author | Pearson, T. C. | en_US |
dc.contributor.author | Çetin, A. | en_US |
dc.date.accessioned | 2016-02-08T09:46:34Z | |
dc.date.available | 2016-02-08T09:46:34Z | |
dc.date.issued | 2012 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T09:46:34Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2012 | en |
dc.identifier.doi | 10.1016/j.compag.2012.02.012 | en_US |
dc.identifier.issn | 0168-1699 | |
dc.identifier.uri | http://hdl.handle.net/11693/21454 | |
dc.language.iso | English | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1016/j.compag.2012.02.012 | en_US |
dc.source.title | Computers and Electronics in Agriculture | en_US |
dc.subject | Correlation features | en_US |
dc.subject | Covariance features | en_US |
dc.subject | Fungus detection on popcorn kernels | en_US |
dc.subject | Image processing | en_US |
dc.subject | SVM | en_US |
dc.subject | Color channels | en_US |
dc.subject | Correlation features | en_US |
dc.subject | Covariance features | en_US |
dc.subject | Covariance matrices | en_US |
dc.subject | Economic loss | en_US |
dc.subject | False positive | en_US |
dc.subject | Image coordinates | en_US |
dc.subject | Image pixels | en_US |
dc.subject | Image processing - methods | en_US |
dc.subject | Image properties | en_US |
dc.subject | Intensity values | en_US |
dc.subject | Kernel image | en_US |
dc.subject | Recognition rates | en_US |
dc.subject | Second derivatives | en_US |
dc.subject | Sorting system | en_US |
dc.subject | Support vector machine (SVM) | en_US |
dc.subject | SVM | en_US |
dc.subject | Image processing | en_US |
dc.subject | Losses | en_US |
dc.subject | Support vector machines | en_US |
dc.subject | Covariance matrix | en_US |
dc.subject | Computer simulation | en_US |
dc.subject | Correlation | en_US |
dc.subject | Crop damage | en_US |
dc.subject | Detection method | en_US |
dc.subject | Fungal disease | en_US |
dc.subject | Fungus | en_US |
dc.subject | Image analysis | en_US |
dc.subject | Maize | en_US |
dc.subject | Matrix | en_US |
dc.subject | Pixel | en_US |
dc.subject | Variance analysis | en_US |
dc.subject | Fungi | en_US |
dc.subject | Nemophila | en_US |
dc.subject | Pseudomugilidae | en_US |
dc.title | Detection of fungal damaged popcorn using image property covariance features | en_US |
dc.type | Article | en_US |
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