Abstract
In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes.
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References
Bhatt, A., Pant, D.: Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI Soc. 30(1), 45–56 (2015)
Zhang, B., Huang, W., Li, W., Zhao, J., Fan, S., Wu, J., Liu, C.: Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res. Int. 62, 326–343 (2014)
Sekhar, N.C., Tudu, B., Koley, C.: A machine vision-based maturity prediction system for sorting of harvested mangoes. IEEE Trans. Instrum. Meas. 63(7), 1722–1730 (2014)
Rodríguez-Pulido, F.J., Gordillo, B., González-Miret, M.L., Heredia, F.J.: Analysis of food appearance properties by computer vision applying ellipsoids to colour data. Comput. Electron. Agric. 99, 108–115 (2013)
van Henten, E.J., Hemming, J., Van Tuijl, B.A.J., Kornet, J.G., Meuleman, J., Bontsema, J., Van Os, E.A.: An autonomous robot for harvesting cucumbers in greenhouses. Auton. Robot. 13(3), 241–258 (2002)
Manickavasagan, A., Al-Mezeini, N., Al-Shekaili, H.: RGB color imaging technique for grading of dates. Sci. Hortic. 175, 87–94 (2014)
Chen, X., Yang, S.: A practical solution for ripe tomato recognition and localization. J. Real-Time Image Process. 8(1), 35–51 (2013)
Gatica, G., Best, S., Ceroni, J., Lefranc, G.: Olive fruits recognition using neural networks. Proc. Comput. Sci. 17, 412–419 (2013)
Zhang, Y., Wang, S., Ji, G., Phillips, P.: Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 (2014)
Chaw, S.W., Hadi, M.S.: A new method for fruits recognition system. In: International Conference on Electrical Engineering and Information, pp. 130–134 (2009)
Bostanci, B., Hagras, H., Dooley, J.: A neuro fuzzy embedded agent approach towards the development of an intelligent refrigerator. In: IEEE International Conference on Fuzzy Systems, pp. 1–8 (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)
Rotaru, C., Graf, T., Zhang, J.: Color image segmentation in HSI space for automotive applications. J. Real-Time Image Process. 3(4), 311–322 (2008)
Zahn, C., Roskies, R.: Fourier descriptors for plane closed curves. IEEE Trans. Comput. C-21(3), 269–284 (1972)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Wang, X., Huang, D.S., Du, J.X., Xu, H., Heutte, L.: Classification of plant leaf images with complicated background. Appl. Math. Comput. 205(2), 916–926 (2008)
Yuan, J.H., Huang, D.S., Zhu, H.D., Gan, Y.: Completed hybrid local binary pattern for texture classification. In: International Conference on Neural Networks, pp. 2050–2057 (2014)
Zhao, Y., Huang, D.S., Jia, W.: Completed local binary count for rotation invariant texture classification. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)
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García-Lamont, F., Cervantes, J., Ruiz, S., López-Chau, A. (2015). Color Characterization Comparison for Machine Vision-Based Fruit Recognition. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_26
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DOI: https://doi.org/10.1007/978-3-319-22180-9_26
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