Abstract
Computer vision techniques are essential for defect segmentation in any automatized fruit-sorting system. Conventional sorting methods employ algorithms that are specific to standard illumination conditions and may produce undesirable results if ideal conditions are not maintained. This paper outlines a scheme that employs adaptive filters for pre-processing to negate the effect of varying illumination followed by defect segmentation using a localized adaptive threshold in an apple sorting experimental system based on a reference image. This technique has been compared with other methods and the results indicate an improved sorting performance. This can also be applied to other fruits with curved contours.
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References
Li, Q., Wang, M., Gu, W.: Computer vision based system for apple surface defect detection. Computers and Electronics in Agriculture 36(2-3), 215–223 (2002)
Yang, Q.: Approach to apple surface feature detection by machine vision. Computers and Electronics in Agriculture 11, 249–264 (1994)
Yang, Q., Marchant, J.A.: Accurate blemish detection with active contour models. Computers and Electronics in Agriculture 14(1), 77–89 (1996)
Leemans, V., Magein, H., Destain, M.F.: Defect segmentation on ‘Jonagold’ apples using colour vision and a Bayesian classification method. Computers and Electronics in Agriculture 23(1), 43–53 (1999)
Molto, E.: Multispectral inspection of citrus in real-time using machine vision. Computers and Electronics in Agriculture 33 (2002)
Chen, Y.-R., Chao, K., Kim, M.S.: Machine vision technology for agricultural applications. Computers and Electronics in Agriculture 12 (2002)
Ariana, D., Guyer, D.E., Shrestha, B.: Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture 50, 148–161 (2006)
Iqbal, M., Sudhakar Rao, P.: Mechanical systems for on-line Fruit Sorting and Grading using Machine Vision Technology. ISOI Journal 34(3)
Huang, C.: Region based illumination-normalization method and system. ROC patent and United States Patent #7263241
Huang, C.: Novel illumination-normalization method based on region information. SPIE proceedings series (2005)
Pei, S.C., Lin, C.Y., Tseng, C.C.: Two-dimensional LMS adaptive linear phase filters. In: ISCAS proceedings (1993)
Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recognition 33(2), 225–236 (2000)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE SMCG, 62–66 (1979)
Shafait, F., Keysers, D., Breuel, T.M.: Efficient implementation of local adaptive thresholding techniques using integral images. In: SPIE proceedings, vol. 6815 (2008)
Badekas, E., Papamarkos, N.: Automatic evaluation and document binarization results. In: CGIM 2008. 10th Iberoamerican congress on pattern recognition, pp. 1005–1014 (2005)
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Kausik, J.L., Aravamudhan, G. (2009). A Robust Algorithm for Fruit-Sorting under Variable Illumination. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_19
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DOI: https://doi.org/10.1007/978-3-642-10546-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10545-6
Online ISBN: 978-3-642-10546-3
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