In this thesis, a feature extraction algorithm based on 2D Local Discriminant Bases (LDB) search is developed for acoustic and hyperspectral data. The developed algorithm extracts the relevant features by both eliminating the irrelevant
ones and/or by merging the ones that do not provide extra information on their own. It is implemented on real world data to separate aflatoxin contaminated or high risk hazelnuts from the sound ones by using impact acoustic and hyperspectral
data. Impact acoustics data is used to sort cracked and intact shell hazelnuts with high classification accuracy. Hypespectral images of the shelled and roasted (SRT) hazelnuts are used for classification and the algorithm extracted the spectral
and spatial-frequency features for that classification. Aflatoxin concentration of the SRT category hazelnuts is automatically decreased to 0.7 ppb from 608 ppb by eliminating the detected contaminated ones.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610192/index.pdf |
| Date | 01 November 2008 |
| Creators | Kalkan, Habil |
| Contributors | Yardimci, Yasemin |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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