Non-destructive testing of textured foods by machine vision

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Date

2009

Author

Beriat, Pelin

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In this thesis, two different approaches are used to extract the relevant features for classifying the aflatoxin contaminated and uncontaminated scaled chili pepper samples: Statistical approach and Local Discriminant Bases (LDB) approach. In the statistical approach, First Order Statistical (FOS) features and Gray Level Cooccurrence Matrix (GLCM) features are extracted. In the LDB approach, the original LDB algorithm is modified to perform 2D searches to extract the most discriminative features from the hyperspectral images by removing irrelevant features and/or combining the features that do not provide sufficient discriminative information on their own. The classification is performed by using Linear Discriminant Analysis (LDA) classifier. Hyperspectral images of scaled chili peppers purchased from various locations in Turkey are used in this study. Correct classification accuracy about 80% is obtained by using the extracted features.

Subject Keywords

Machine Vision.

URI

http://etd.lib.metu.edu.tr/upload/12610405/index.pdf
https://hdl.handle.net/11511/18459

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Graduate School of Informatics, Thesis

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Citation Formats

P. Beriat, “Non-destructive testing of textured foods by machine vision,” M.S. - Master of Science, Middle East Technical University, 2009.