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 imagingReferences
R. C. Gonzalez, R. E. Woods, Digital Image Processing, Prentice-Hall Inc., New Jersey, 2002.
Google Scholar
R. Tadeusiewicz, Komputerowa analiza i przetwarzanie obrazów, Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków 1997.
Google Scholar
B. Mirkin, Clustering: A Data Recovery Approach, Chapman and Hall/CRC, 2012.
Google Scholar
D. F. Morrison, Multivariate Statistical Methods, Brooks/Cole Thomson Learning, Belmont, California, 2005.
Google Scholar
M. Romaniuk, O. Hryniewicz, Interval based, nonparametric approach for resampling of fuzzy numbers. 2019, Soft Computing, 23 (14), 5883–5903.
Google Scholar
J. Koronacki, J. Ćwik, Statystyczne systemy uczące się, WNT 2008.
Google Scholar
M. Krzyśko, W. Wołyński, T. Górecki, M. Skorzybut, Systemy uczące się. WNT, Warszawa, 2008.
Google Scholar
H. Czachor, M. Charytanowicz, S. Gonet, J. Niewczas, G. Józefaciuk, L. Lichner, Impact of long term mineral and organic fertilization on water stability, wettability and porosity of aggregates of two silt loamy soils. 2015, European Journal of Soil Science, 66 (3), 577–588.
Google Scholar
M. Klatka, E. Grywalska, M. Partyka, M. Charytanowicz, E. Kiszczak-Bochyńska, J. Roliński: Th17 and Treg cells in adolescents with Graves' disease. Impact of treatment with methimazole on these cell subsets, 2014, Autoimmunity, 47 (3), 201-211.
DOI: https://doi.org/10.3109/08916934.2013.879862
Google Scholar
P. Kulczycki, P. A. Kowalski, Bayes Classification for Nonstationary Patterns, International Journal of Computational Methods, 2015, 12, ID 1550008.
DOI: https://doi.org/10.1142/S0219876215500085
Google Scholar
T. Guz, Z. Kobus, E. Kusińska, R. Nadulski, Morphometric features of rye caryopses stored in a silo, Inżynieria Rolnicza Agricultural Engineering, 2012, 1 (4), 71-79.
Google Scholar
P. Zapotoczny, Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture. Journal of Cereal Science, 2012, 54, 60-68.
DOI: https://doi.org/10.1016/j.jcs.2011.02.012
Google Scholar
M. Charytanowicz, J. Niewczas, P. Kulczycki, P. A. Kowalski, S. Łukasik, Discrimination of Wheat Grain Varieties Using X-ray Images. Information Technologies in Biomedicine, E. Pietka, P. Badura, J. Kawa, W. Więcławek (red.), Advances in Intelligent Systems and Soft Computing, Springer, 2016, 471, 39-50.
DOI: https://doi.org/10.1007/978-3-319-39796-2_4
Google Scholar
M. Charytanowicz, P. Kulczycki, P. A. Kowalski, S. Łukasik, R. Czabak-Garbacz, An Evaluation of Utilizing Geometric Features for Wheat Grain Classification using X-ray Images. Computers and Electronics in Agriculture, 2018, 144, 260-268.
DOI: https://doi.org/10.1016/j.compag.2017.12.004
Google Scholar
J. Niewczas, A. Strumiłło, Szczypiński, P. Makowski, W. Woźniak, Computer system for analysis of x-ray image of wheat grains, International Agrophysics 1999.
Google Scholar
G. D. Jasmin, Shape based Object Classification Rusing Knowledge Vector Code International Journal of Innovative Research in Computer and Communication Engineering, 2017, 5 (7), 13440.
Google Scholar
D. Zhang, G. Lu, Review of shape representation and description techniques. Pattern Recognition, 2004, 37, 1-19.
DOI: https://doi.org/10.1016/j.patcog.2003.07.008
Google Scholar
M. Zhu, T. J. Hastie, Feature Extraction for Nonparametric Discriminant Analysis. Journal of Computational and Graphical Statistics, 2003, 12(1), 101-120.
DOI: https://doi.org/10.1198/1061860031220
Google Scholar
Visual Studio 2017 – Now Ready for Your Windows Application Development Needs, https://blogs.windows.com/windowsdeveloper/2017/03/07/visual-studio-2017-now-ready-windows-application-development-needs/, [22.04.2020].
Google Scholar
Qt Software, http://doc.qt.io/, [01.04.2020].
Google Scholar
C++ Programming Language, https://www.techopedia.com/definition/26184/c-programming-language, [22.04.2020].
Google Scholar
About OpenCV, https://opencv.org/about/, [22.04.2020].
Google Scholar
Authors
Małgorzata CharytanowiczLublin University of Technology Poland
Statistics
Abstract views: 322PDF downloads: 318
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.