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On-line Access: 2010-01-10

Received: 2009-12-29

Revision Accepted: 2010-04-29

Crosschecked: 2010-12-06

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.1 P.54-61

http://doi.org/10.1631/jzus.C0910797


New separation algorithm for touching grain kernels based on contour segments and ellipse fitting


Author(s):  Lei Yan, Cheol-Woo Park, Sang-Ryong Lee, Choon-Young Lee

Affiliation(s):  School of Mechanical Engineering, Kyungpook National University, Daegu 702-701, Korea, School of Technology, Beijing Forestry University, Beijing 100083, China

Corresponding email(s):   cylee@knu.ac.kr

Key Words:  Separation algorithm, Touching grains, Contour segments, Ellipse fitting


Lei Yan, Cheol-Woo Park, Sang-Ryong Lee, Choon-Young Lee. New separation algorithm for touching grain kernels based on contour segments and ellipse fitting[J]. Journal of Zhejiang University Science C, 2011, 12(1): 54-61.

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%J Journal of Zhejiang University SCIENCE C
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T1 - New separation algorithm for touching grain kernels based on contour segments and ellipse fitting
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910797


Abstract: 
A new separation algorithm based on contour segments and ellipse fitting is proposed to separate the ellipse-like touching grain kernels in digital images. The image is filtered and converted into a binary image first. Then the contour of touching grain kernels is extracted and divided into contour segments (CS) with the concave points on it. The next step is to merge the contour segments, which is the main contribution of this work. The distance measurement (DM) and deviation error measurement (DEM) are proposed to test whether the contour segments pertain to the same kernel or not. If they pass the measurement and judgment, they are merged as a new segment. Finally with these newly merged contour segments, the ellipses are fitted as the representative ellipses for touching kernels. To verify the proposed algorithm, six different kinds of Korean grains were tested. Experimental results showed that the proposed method is efficient and accurate for the separation of the touching grain kernels.

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Reference

[1]Aguilera, J.M., Cipriano, A., Eraña, M., Lillo, I., Mery, D., Soto, A., 2007. Computer Vision for Quality Control in Latin American Food Industry: a Case Study. Int. Conf. on Computer Vision: Workshop on Computer Vision Applications for Developing Countries, p.1-11.

[2]Bailey, D.G., 1992. Segmentation of Touching Objects. 7th NZ Image Processing Workshop, p.1-6.

[3]Brosnan, T., Sun, D.W., 2004. Improving quality inspection of food products by computer vision: a review. J. Food Eng., 61(1):3-16.

[4]Carter, R.M., Yan, Y., Tomlins, K., 2006. Digital imaging based classification and authentication of granular food products. Meas. Sci. Technol., 17(2):235-240.

[5]Cheng, F., Ying, Y.B., 2004a. Machine vision inspection of rice seed based on Hough transform. J. Zhejiang Univ.-Sci., 5(6):663-667.

[6]Cheng, F., Ying, Y.B., 2004b. Variety recognition of rice seeds using image analysis and artificial neural network. SPIE, 5587:71.

[7]Davidson, V.J., Ryks, J., Chu, T., 2001. Fuzzy models to predict consumer ratings for biscuits based on digital features. IEEE Trans. Fuzzy Syst., 9(1):62-67.

[8]Fitzgibbon, A., Pilu, M., Fisher, R.B., 1999. Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell., 21(5):476-480.

[9]Gao, H., Wang, Y., Ge, P., 2007. Research on Segmentation Algorithm of Adhesive Plant Grain Image. 8th Int. Conf. on Electronic Measurement and Instruments, 2:927-930.

[10]Hobson, D.M., Carter, R.M., Yan, Y., 2007. Characterisation and Identification of Rice Grains Through Digital Image Analysis. IEEE Instrumentation and Measurement Technology Conf. Proc., p.1-5.

[11]Liu, Z., Cheng, F., Ying, Y., Rao, X., 2005. Identification of rice seed varieties using neural network. J. Zhejiang Univ.-Sci., 6B(11):1095-1100.

[12]Lu, J., Tan, J., Gerrard, D.E., 1997. Pork Quality Evaluation by Image Processing. ASAE Annual Int. Meeting Technical Papers. MI, USA.

[13]Lu, J., Tan, J., Shatadal, P., Gerrard, D.E., 2000. Evaluation of pork color by using computer vision. Meat Sci., 56(1):57-60.

[14]Luo, X., Jayas, D.S., Symons, S.J., 1999. Identification of damaged kernels in wheat using a colour machine vision system. J. Cereal Sci., 30(1):49-59.

[15]Majumdar, S., Jayas, D.S., 1999. Classification of bulk samples of cereal grains using machine vision. J. Agric. Eng. Res., 73(1):35-47.

[16]Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern., 9(1):62-66.

[17]Paliwal, J., Visen, N.S., Jayas, D.S., White, N.D.G., 2003. Cereal grain and dockage identification using machine vision. Biosyst. Eng., 85(1):51-57.

[18]Paliwal, J., Borhan, M.S., Jayas, D.S., 2004a. Classification of cereal grains using a flatbed scanner. Can. Biosyst. Eng., 46:3.1-3.5.

[19]Paliwal, J., Jayas, D.S., Visen, N.S., White, N.D.G., 2004b. Feasibility of a machine-vision based grain cleaner. Appl. Eng. Agric., 20(2):245-248.

[20]Paulus, I., Schrevens, E., 1999. Shape characterisation of new apple cultivars by Fourier expansion of digital images. J. Agric. Eng. Res., 72(2):113-118.

[21]Shatadal, P., Jayas, D.S., Bulley, N.R., 1995. Digital image analysis for software separation and classification of touching grains. I. Disconnect algorithm. Trans. ASAE, 38(2):635-643.

[22]Shearer, S.A., Payne, F.A., 1990. Color and defect sorting of bell peppers using machine vision. Trans. ASAE, 33(6):2045-2050.

[23]Sun, D.W., 2000. Inspecting pizza topping percentage and distribution by a computer vision method. J. Food Eng., 44(4):245-249.

[24]Visen, N.S., Shashidhar, N.S., Paliwal, J., Jayas, D.S., 2001. Identification and segmentation of occluding groups of grain kernels in a grain sample image. J. Agric. Eng. Res., 79(2):159-166.

[25]Wang, W., Paliwal, J., 2006. Separation and identification of touching kernels and dockage components in digital images. Can. Biosyst. Eng. 48:7.1-7.7.

[26]Zayas, I., Pomeranz, Y., Lai, F.S., 1989. Discrimination of wheat and nonwheat components in grain samples by image analysis. Cereal Chem., 66(3):233-237.

[27]Zayas, I.Y., Martin, C.R., Steele, J.L., Katsevich, A., 1996. Wheat classification using image analysis and crush force parameters. Trans. ASAE, 39(6):2199-2204.

[28]Zhang, G., Jayas, D.S., White, N.D.G., 2005a. Separation of touching grain kernels in an image by ellipse fitting algorithm. Biosyst. Eng., 92(2):135-142.

[29]Zhang, G., Deng, J., Jayas, D.S., 2005b. Separation Touching Grain Kernel in Machine Vision Application with Hough Transform and Morphological Transform. CSAE/SCGR Meeting, Paper No. 05-006.

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