Object classification using X-ray images

Piotr Nowosad

piotr.nowosad@pollub.edu.pl
(Poland)

Małgorzata Charytanowicz


Lublin University of Technology (Poland)

Abstract

The main aim of the presented research was to assess the possibility of utilizing geometric features in object classification. Studies were conducted using X-ray images of kernels belonging to three different wheat varieties: Kama, Canadian and Rosa. As a part of the work, image processing methods were used to determine the main geometric grain parameters, including the kernel area, kernel perimeter, kernel length and kernel width. The results indicate significant differences between wheat varieties, and demonstrates the importance of their size and shape parameters in the classification process. The percentage of correctness of classification was about 92% when the k-Means algorithm was used. A classification rate of 93% was obtain using the K-Nearest Neighbour and Support Vector Machines. Herein, the Rosa variety was better recognized, whilst the Canadian and Kama varieties were less successfully differentiated.


Keywords:

object classification, geometric features, image processing, X-ray imaging

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Published
2020-06-30

Cited by

Nowosad, P., & Charytanowicz, M. (2020). Object classification using X-ray images. Journal of Computer Sciences Institute, 15, 206–213. https://doi.org/10.35784/jcsi.1720

Authors

Piotr Nowosad 
piotr.nowosad@pollub.edu.pl
Poland

Authors

Małgorzata Charytanowicz 

Lublin University of Technology Poland

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