Object classification using X-ray images - Nowosad, Piotr; Charytanowicz, Małgorzata (Journal of Computer Sciences Institute, 2020)
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.