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Leaf Recognition Based on the Combination of Wavelet Transform and Gaussian Interpolation

  • Xiao Gu
  • Ji-Xiang Du
  • Xiao-Feng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3644)

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.

Keywords

Wavelet Transform Detail Image Spline Wavelet Correct Recognition Rate Plant Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiao Gu
    • 1
  • Ji-Xiang Du
    • 1
    • 2
  • Xiao-Feng Wang
    • 1
  1. 1.Institute of Intelligent MachinesChinese Academy of SciencesHefeiChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina

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