Abstract
In this paper, a new approach for leaf recognition using the result of segmentation of leaf’s skeleton based on the combination of wavelet transform (WT) and Gaussian interpolation is proposed. And then the classifiers, a nearest neighbor classifier (1-NN), a K-nearest neighbor classifier (k-NN) and a radial basis probabilistic neural network (RBPNN) are used, based on run-length features (RF) extracted from the skeleton to recognize the leaves. Finally, the effectiveness and efficiency of the proposed method is demonstrated by several experiments. The results show that the skeleton can be successfully and obviously extracted from the whole leaf, and the recognition rates of leaves based on their skeleton can be greatly improved.
This work was supported by the National Natural Science Foundation of China (Nos.60472111 and 60405002).
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Gu, X., Du, JX., Wang, XF. (2005). Leaf Recognition Based on the Combination of Wavelet Transform and Gaussian Interpolation. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_27
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DOI: https://doi.org/10.1007/11538059_27
Publisher Name: Springer, Berlin, Heidelberg
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