Feature Selection From Hyperspectral Imaging Through Discriminant Function For Guava Defects Detection
| dc.contributor.author | Tan, Suo Ching | |
| dc.date.accessioned | 2018-02-02T07:34:06Z | |
| dc.date.available | 2018-02-02T07:34:06Z | |
| dc.date.issued | 2017-06 | |
| dc.description.abstract | Development of technology makes hyperspectral imaging commonly used for defects detection. In this research, a hyperspectral imaging system was setup with the study of lighting and compare the selected feature through different feature selection methods. Light Emitting Diode (LED) and fluorescent light sources was examined and found that both light sources having an almost identical reflectance pattern. The peak intensity of the fluorescent light source is 4.5 times higher than this particular LED. By considering the signal to noise ratio, fluorescent light source was selected for subsequent experiments. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select several features for further processing. Different training methods were used in this research. The first method which refers to conventional method (CM) is to feed the training sample as a whole dataset into the selection. The second method is to feed the training sample by separate the image into two parts (TPTM) which refer to a higher reflectivity area and lower reflectivity area. Four matrixes of validation were done, which are SFS-LD-CM, SFS-LD-TPTM, SFS-QD-CM and SFS-QD-TPTM. These methods were evaluated by F1-score. With 48 defect areas, experiment shown that F1-score of SFS-LD-TPTM has a higher detection rate which is 0.8 in F1-score, compared with 0.78 by SFS-QDCM and 0.72 for both SFS-LD-CM and SFS-QD-TPTM. This concludes that by selecting the features with two training samples able to minimize the impact of uneven illumination for this particular system and hence having higher correct detection rate in guava defects detection. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/5491 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Feature selection from Hyperspectral imaging through | en_US |
| dc.subject | Discriminant function For guava defects detection | en_US |
| dc.title | Feature Selection From Hyperspectral Imaging Through Discriminant Function For Guava Defects Detection | en_US |
| dc.type | Thesis | en_US |
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