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PhD scholarship (P.O.R. Sardegna F.S.E. Operational
Programme of the Autonomous Region of Sardinia,
European Social Fund 2007-2013 - Axis IV Human
Resources, Objective l.3, Line of Activity l.3.1.). We
wish to thank Wu et al. for having made available the
Dataset on which we could test our method.
REFERENCES
Arribas, J. I., Snchez-Ferrero, G. V., Ruiz-Ruiz, G., and
Gmez-Gil, J. (2011). Leaf classification in sunflower
crops by computer vision and neural networks. Com-
puters and Electronics in Agriculture, 78(1):9 – 18.
Chaki, J. and Parekh, R. (2011). Plant leaf recognition using
shape based features and neural network classifiers.
International Journal of Advanced Computer Science
and Applications, 2(10):41 – 47.
Cheng, S.-C., Jhou, J.-J., and Liou, B.-H. (2007). Pda plant
search system based on the characteristics of leaves
using fuzzy function. In New Trends in Applied Ar-
tificial Intelligence, volume 4570 of Lecture Notes in
Computer Science, pages 834–844. Springer.
Du, J.-X., Huang, D.-S., Wang, X.-F., and Gu, X. (2006).
Computer-aided plant species identification based on
leaf shape matching technique. Transactions of the
Institute of Measurement and Control, 28(3):275–285.
Du, J.-X., Wang, X.-F., and Zhang, G.-J. (2007). Leaf shape
based plant species recognition. Applied Mathematics
and Computation, 185(2):883 – 893.
Ehsanirad, A. (2010). Plant classification based on leaf
recognition. International Journal of Computer Sci-
ence and Information Security, 8(4):78–81.
Gao, L., Lin, X., Zhao, W., Chen, S., and Huang, H.
(2010a). An algorithm of excising leafstalk while
keeping its main body intact for leaf recognition. In
Image and Signal Processing (CISP), 2010 3rd Inter-
national Congress on, volume 6, pages 2732–2736.
Gao, L., Lin, X., Zhong, M., and Zeng, J. (2010b). A neural
network classifier based on prior evolution and itera-
tive approximation used for leaf recognition. In Nat-
ural Computation (ICNC), 2010 Sixth International
Conference on, volume 2, pages 1038–1043.
Gonzalez, R. C., Woods, R. E., and Eddins, S. L. (2004).
Digital Image Processing Using MATLAB. Pearson
Prentice Hall Pearson Education, New Jersey, USA,
1st edition.
Haralick, R., Shanmugam, K., and Dinstein, I. (1973).
Textural features for image classification. Systems,
Man and Cybernetics, IEEE Transactions on, SMC-
3(6):610–621.
Hu, M.-K. (1962). Visual pattern recognition by moment
invariants. Information Theory, IRE Transactions on,
8(2):179–187.
Im, C., Nishida, H., and Kunii, T. (1998). Recognizing plant
species by leaf shapes-a case study of the acer family.
In 14th International Conference on Pattern Recogni-
tion, volume 2, pages 1171–1173.
Kadir, A., Nugroho, L. E., Susanto, A., and Santosa, P. I.
(2011). A comparative experiment of several shape
methods in recognizing plants. International Jour-
nal of Computer Science & Information Technology,
3(3):256–263.
Kulkarni, A. H., Rai, D. H. M., Jahagirdar, D. K. A., and
Upparamani, P. S. (2013). A leaf recognition tech-
nique for plant classification using rbpnn and zernike
moments. International Journal of Advanced Re-
search in Computer and Communication Engineering,
2(1):82–93.
Lee, C.-L. and Chen, S.-Y. (2006). Classification of leaf
images. International Journal of Imaging Systems and
Technology, 16(1):15–23.
Lee, K. B. and Hong, K. S. (2013). An implementa-
tion of leaf recognition system using leaf vein and
shape. International Journal of Bio-Science and Bio-
Technology, 5(2):57–65.
Lin, H. and Peng, H. (2008). Machine recognition for
broad-leaved trees based on synthetic features of
leaves using probabilistic neural network. In Com-
puter Science and Software Engineering, 2008 Inter-
national Conference on, volume 4, pages 871–877.
Machado, B. B., Casanova, D., Gonalves, W. N., and Bruno,
O. M. (2013). Partial differential equations and frac-
tal analysis to plant leaf identification. Journal of
Physics: Conference Series, 410(1).
Man, Q.-K., Zheng, C.-H., Wang, X.-F., and Lin, F.-Y.
(2008). Recognition of plant leaves using support
vector machine. In Advanced Intelligent Computing
Theories and Applications. With Aspects of Contem-
porary Intelligent Computing Techniques, volume 15
of Communications in Computer and Information Sci-
ence, pages 192–199. Springer Berlin Heidelberg.
Pauwels, E. J., de Zeeuw, P. M., and Ranguelova, E. (2009).
Computer-assisted tree taxonomy by automated image
recognition. Engineering Applications of Artificial In-
telligence, 22(1):26–31.
Singh, K., Gupta, I., and Gupta, S. (2010). Svm-bdt pnn
and fourier moment technique for classification of leaf
shape. International Journal of Signal Processing, Im-
age Processing and Pattern Recognition, 3(4):67–78.
Valliammal, N. and Geethalakshmi, S. N. (2011). Hybrid
image segmentation algorithm for leaf recognition and
characterization. In International Conference on Pro-
cess Automation, Control and Computing, pages 1–6.
Wang, Z., Chi, Z., and Feng, D. (2003). Shape based leaf
image retrieval. Vision, Image and Signal Processing,
IEEE Proceedings, 150(1):34–43.
Wu, Q., Zhou, C., and Wang, C. (2006). Feature extraction
and automatic recognition of plant leaf using artificial
neural network. Avances en Ciencias de la Computa-
cion, pages 5–16.
Wu, S., Bao, F., Xu, E., Wang, Y.-X., Chang, Y.-F., and
Xiang, Q.-L. (2007). A leaf recognition algorithm for
plant classification using probabilistic neural network.
In IEEE International Symposium on Signal Process-
ing and Information Technology, pages 11–16.
Zhang, S. and Lei, Y.-K. (2011). Modified locally linear dis-
criminant embedding for plant leaf recognition. Neu-
rocomputing, 74(1415):2284 – 2290.
Zhang, X. and Zhang, F. (2008). Images features extraction
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