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International Conference Image Analysis and Recognition

ICIAR 2008: Image Analysis and Recognition pp 1071–1080Cite as

Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5112)

Abstract

The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic algorithms. The image segmentation relies on the combination of band selection techniques and pixel classification methods such as classification and regression trees and linear discriminant analysis.

Keywords

  • feature selection
  • hyperspectral imaging
  • pixel classification
  • fruit inspection

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  • DOI: 10.1007/978-3-540-69812-8_107
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References

  1. Blasco, J., Cubero, S., Arias, R., Gómez, J., Juste, F., Moltó., E.: Development of a computer vision system for the automatic quality grading of mandarin segments. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 460–466. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  2. Blasco, J., Aleixos, N., Moltó, E.: Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 81(3), 535–543 (2007)

    CrossRef  Google Scholar 

  3. Blasco, J., Aleixos, N., Gómez-Sanchis, J., Moltó, E.: Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83(3), 384–393 (2007)

    CrossRef  Google Scholar 

  4. Chang, C.I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, New York (2003)

    Google Scholar 

  5. Chen, R.K., Yang, C.M.: Estimating rice growth using ground-based hyperspectral reflectance data and simulated SPOT broad band data. Journal of Agricultural Research of China 51(4), 1–18 (2002)

    Google Scholar 

  6. Martínez-Sotoca, J., Plá, F.: Hyperspectral Data Selection from Mutual Information Between Image Bands. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 853–861. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  7. Yang, C., Everitt, J.H., Bradford, J.M.: Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE 47(3), 915–924 (2004)

    MATH  Google Scholar 

  8. Yao, H., Tian, L.: A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Transactions on Geoscience and Remote Sensing 41(6), 1469–1478 (2006)

    Google Scholar 

  9. Steingberg, P., Colla, P.: CART. Classification and Regression Trees. Salford Systems. San Diego (1997)

    Google Scholar 

  10. Gómez-Chova, L., Calpe, J., Soria, E., Camps-Valls, G., Martín, J.D., Moreno, J.: CART-based feature selection of hyperspectral images for crop cover classification. In: ICIP Proceedings of the International Conference on Image Processing, vol. 3, pp. 589–592 (2003)

    Google Scholar 

  11. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley-Interscience, New York (2000)

    Google Scholar 

  12. Bajksy, P., Kooper, R.: Prediction accuracy of color imagery from hyperspectral imagery (last accessed January 2008), http://algdocs.ncsa.uiuc.edu/PB-20050328-2.pdf

  13. Gómez-Sanchis, J., Moltó, E., Camps-Valls, G., Gómez-Chova, L., Aleixos, N., Blasco, J.: Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering 85(2), 191–200 (2008)

    CrossRef  Google Scholar 

  14. Blum, A.V., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)

    CrossRef  MathSciNet  MATH  Google Scholar 

  15. Kohavi, R., John, G.H.: Wrappers for features subset selection. Artificial Intelligence 97, 273–324 (1997)

    CrossRef  MATH  Google Scholar 

  16. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston (1989)

    MATH  Google Scholar 

  17. Breiman, L., Friedman, J., Olshen, R., Stone, J.: Classification and regression trees. CRC Press, Boca Raton (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Centro de AgroIngeniería. Instituto Valenciano de Investigaciones Agrarias (IVIA), Ctra. Moncada-Náquera km 5, Moncada, 46113, (Valencia), Spain

    J. Gómez-Sanchis & E. Moltó

  2. Digital Signal Processing Group, (GPDS). Electronic Engineering Department, University of Valencia, Doctor Moliner 50, Burjassot, 46100, (Valencia), Spain

    G. Camps-Valls, L. Gómez-Chova & J. Blasco

  3. Department of Graphics Engineering, DIG – ETSII, Polytechnic University of Valencia (UPV), 46022, Valencia, Spain

    N. Aleixos

Authors
  1. J. Gómez-Sanchis
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  2. G. Camps-Valls
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  3. E. Moltó
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  4. L. Gómez-Chova
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  5. N. Aleixos
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  6. J. Blasco
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    © 2008 Springer-Verlag Berlin Heidelberg

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    Gómez-Sanchis, J., Camps-Valls, G., Moltó, E., Gómez-Chova, L., Aleixos, N., Blasco, J. (2008). Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_107

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    • DOI: https://doi.org/10.1007/978-3-540-69812-8_107

    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-69811-1

    • Online ISBN: 978-3-540-69812-8

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