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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Chang, C.I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Springer, New York (2003)
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)
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)
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)
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)
Steingberg, P., Colla, P.: CART. Classification and Regression Trees. Salford Systems. San Diego (1997)
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)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley-Interscience, New York (2000)
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
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)
Blum, A.V., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)
Kohavi, R., John, G.H.: Wrappers for features subset selection. Artificial Intelligence 97, 273–324 (1997)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston (1989)
Breiman, L., Friedman, J., Olshen, R., Stone, J.: Classification and regression trees. CRC Press, Boca Raton (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)