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Automatic selection and fusion of color spaces for image thresholding

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Abstract

Automatic selection of color models has a great significance for machine vision purposes like image segmentation, object recognition, etc. Typically, selection of a proper color model is a problem that can just solve by testing the models on the target one by one. To achieve a proper color model, in this article, we propose a new method which is shaped on the basis of clustering and relation among models. The proposed method is verified experimentally for two different images (in thresholding purpose). The experimental results show that this method has a suitable power for automatic purposes.

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References

  1. Loch, P., Thomsen, K., Mikkelsen, P.: Full color image analysis as a tool for quality control and process development in the food industry, Paper No. 973006, ASAE, St. Joseph, MI (1997)

  2. Abdullah Z.M., Aziz A.S., Dos-Mohamed A.M.: Quality inspection of bakery products using a colour-based machine vision system. J. Food Qual. 23(1), 39–50 (2000)

    Article  Google Scholar 

  3. Zion B., Shklyar A., Karplus I.: Sorting fish by computer vision. Comput. Electron. Agric. 23(3), 175–187 (1999)

    Article  Google Scholar 

  4. Andreadis I.: Modelling and evaluating colour information for robot vision. Mechatronics 9, 429–446 (1999)

    Article  Google Scholar 

  5. Balaban M.O., Kristinsson H.G., Otwell W.S.: Evaluation of color parameters in a machine vision analysis of carbon monoxide-treated fish-part 1: Fresh tuna. J. Aquat. Food Prod. Technol. 14, 5–24 (2005)

    Article  Google Scholar 

  6. Klinker G.J., Shafer S.A., Kanade T: A physical approach to color image understanding. Int. J. Comput. Vis. 4(1), 7–38 (1990)

    Article  Google Scholar 

  7. Shapiro L.G., Stockman G.C.: Computer Vision. Prentice Hall, Englewood Cliffs (2002)

    Google Scholar 

  8. Kamel M., Zhao A.: Extraction of binary character/graphics images from grayscale document images. Graph. Models Image Process. 55(3), 203–217 (1993)

    Article  Google Scholar 

  9. Abak, T., Baris, U., Sankur, B.: The performance of thresholding algorithms for optical character recognition. In: International Conference on Document Analysis Recognition: ICDAR’97, pp. 697–700 (1997)

  10. Trier O.D., Jain A.K.: Goal-directed evaluation of binarization methods. In: IEEE Trans. Pattern Anal. Mach. Intell. PAMI 17, 1191–1201 (1995)

    Google Scholar 

  11. Bhanu B.: Automatic target recognition: state of the art survey. In: IEEE Trans. Aerosp. Electron. Syst. AES-22, 364–379 (1986)

    Google Scholar 

  12. Sezgin M., Tasaltin R.: A new dichotomization technique to multilevel thresholding devoted to inspection applications. Pattern Recogn. Lett. 21, 151–161 (2000)

    Article  Google Scholar 

  13. Sezgin, M., Sankur, B.: Comparison of thresholding methods for non-destructive testing applications. In: IEEE ICIP’2001, International Conference on Image Processing, pp. 764–767 (2001)

  14. Russ J.C.: Automatic discrimination of features in gray-scale images. J. Microsc. 148(3), 263–277 (1987)

    Article  Google Scholar 

  15. Sieracki M.E., Reichenbach S.E., Webb K.L.: Evaluation of automated threshold selection methods for accurately sizing microscopic fluorescent cells by image analysis. Appl. Environ. Microbiol. 55, 2762–2772 (1989)

    Google Scholar 

  16. Benedek, C., Sziranyi, T.: Study on color space selection for detecting cast shadows in video surveillance. Int. J. Imaging Syst. Technol. Special Issue on Applied Color Image Processing vol. 17, no. 3, pp. 190–201. Wiley, London (2007)

  17. Busin, L., Shi, J., Vandenbroucke, N., Macaire, L.: Ecole d’Ing, color space selection for color image segmentation by spectral clustering. Signal and Image Processing Applications (ICIPA) (2009)

  18. Lysaker M., Lundervold A., Tai X.: Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. In: IEEE Trans. Image Process. 12(12), 1579–1590 (2003)

    Google Scholar 

  19. Arce G.R.: Nonlinear Signal Processing: A Statistical Approach. Wiley, New Jersey (2005) ISBN: 978-0-471-67624-9

    Google Scholar 

  20. http://fourier.eng.hmc.edu/e161/lectures/smooth_sharpen/node3.html

  21. Noordam, J.C., Otten, G.W.: A Color Vision System for High Speed Sorting of Potatoes. Agrotechnological Research Institute No: 00-AE-002, Netherlands (2000)

  22. Noordam J.C., van den Broek W.H.A.M., Buydens L.M.C.: Multivariate image segmentation with cluster size insensitive Fuzzy C-means. Chemom. Intell. Lab. Syst. 64, 65–78 (2002)

    Article  Google Scholar 

  23. http://www.fho-emden.de/%7Ehoffmann. Gernot Hoffmann 20 Dec 2002

  24. Davies, E.: Machine Vision Theory, Algorithms and Practicalities (Signal Processing and its Applications). ISBN: 0122060938, Morgan Kaufmann, USA (2005)

  25. Nacereddine, N., Zelmat, M., Belaïfa, S.S., Tridi, M.: Weld defect detection in industrial radiography based digital image processing. In: International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, Tunisia (2005)

  26. Priyono A.: Generation of fuzzy rules with subtractive. J. Technol. 43, 143–153 (2005) Malaysia

    Google Scholar 

  27. The Hutchinson Dictionary of World History. Helicon Publishing, Oxford (1999)

  28. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspires by imperialistic competition. 2007 IEEE Congress on Evolutionary Computation, Sigapore (2007)

  29. Palmer, R.R.: A History of the Modern World. Alfred A. Knopf, New York 1964 ©1956

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

    MathSciNet  Google Scholar 

  31. Kapur J.N., Sahoo P.K., Wong A.K.C.: A new method for graylevel picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29, 273–285 (1985)

    Article  Google Scholar 

  32. Rosin P.L.: Unimodal thresholding. Pattern Recognit. 34(11), 2083–2096 (2001)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Young Researchers Club, Majlesi Branch, Islamic Azad University, Isfahan, Iran

    Navid Razmjooy

  2. Department of Electrical Engineering, Hatef Higher Education Institute, Zahedan, Iran

    B. Somayeh Mousavi

  3. Young Researchers Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran

    Mohsen Khalilpour & Hossein Hosseini

Authors
  1. Navid Razmjooy
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  2. B. Somayeh Mousavi
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  3. Mohsen Khalilpour
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  4. Hossein Hosseini
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Corresponding author

Correspondence to B. Somayeh Mousavi.

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Razmjooy, N., Mousavi, B.S., Khalilpour, M. et al. Automatic selection and fusion of color spaces for image thresholding. SIViP 8, 603–614 (2014). https://doi.org/10.1007/s11760-012-0303-7

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  • DOI: https://doi.org/10.1007/s11760-012-0303-7

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