Proceedings of the 3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES2014) pp 91-97 | Cite as
An Effective Method for Classification of White Rice Grains Using Various Image Processing Techniques
Abstract
This chapter presents an algorithm for classifying grains of white rice by using image processing. Each image size is acquired via a digital camera. The resolution is 720 × 480 pixels. The algorithm begins with improving grain images, converting these images into binary images by using Otsu’s method, removing noise from the binary images by applying the morphological method with square structural elements, detecting each grain boundary by using the Canny operator, and determining the length of each grain by using the Euclidean method. Next, the grain length is used for classifying the rice grains according to the Rice Standards of Thailand. The testing results from processing 500 grain images; one grain per image, the algorithm provides good performance with the mean absolute error of 0.01 mm in length. For 300 grain images with some grains per image, the algorithm provides good classification with an average accuracy of 99.33 %.
Keywords
Image processing Morphological Classification White rice grainsNotes
Acknowledgment
This project is financially supported by Hands-on Research Rajamangala University of Technology Lanna.
References
- 1.Ajmal, A., Hussain, I.M.: Vehicle detection using morphological image processing technique. Paper presented at the international conference on multimedia computing and information technology (MCIT), 2–4 March 2010Google Scholar
- 2.Lin, J., Luo, S., Li, Q., Zhang, H., Ren, S.: Real-time rail head surface defect detection: a geometrical approach. Paper presented at the IEEE international symposium on industrial electronics, ISIE 2009, 5–8 July 2009Google Scholar
- 3.Sim, K.S., Kho, Y.Y., Tso, C.P.: Application of contrast enhancement bilateral closing top-hat Otsu thresholding (CEBICTOT) technique on crack images. Paper presented at the 7th IEEE international conference on cybernetic intelligent systems, CIS 2008, 9–10 September 2008Google Scholar
- 4.Liang, P., Zhiwei, X., Jiguang, D.: Fast normalized cross-correlation image matching based on multiscale edge information. Paper presented at the 2010 international conference on computer application and system modeling (ICCASM), 22–24 October 2010Google Scholar
- 5.Alasdair, M.: Introduction to Digital Image Processing with Matlab. Thomson Course Technology, Boston (2004)Google Scholar
- 6.Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
- 7.Baraghimian, G.A.: Connected component labeling using self-organizing feature maps. Paper presented at the proceedings of the 13th annual international conference on computer software and applications conference, COMPSAC 89, 20–22 September 1989Google Scholar
- 8.Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2003)Google Scholar
- 9.Bing, W., ShaoSheng, F.: An improved canny edge detection algorithm. Paper presented at the second international workshop on computer science and engineering, WCSE’09, 28–30 October 2009Google Scholar
- 10.Han, J., Kamber, M.: Data Mining Concepts and Techniques. Elsevier/Morgan Kaufmann, Amsterdam/San Francisco (2006)MATHGoogle Scholar
- 11.Canny, J.: A computational approach to edge detection. [Online]. http://ieeexplore.ieee.org/stamp/Stamp.jsp?tp=&arnumber=4767851 (2010)
- 12.Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Saddle River (2002)Google Scholar