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Leaf Image Recognition Using Fourier Transform Based on Ordered Sequence

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Intelligent Computing Technology (ICIC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

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Abstract

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition.

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References

  1. Wang, X.F., Huang, D.S., Du, J.X.: Classification of Plant Leaf Images with Complicated Background. Applied Mathematics and Computation 205, 916–926 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  2. Wang, X.-F., Du, J.-X., Zhang, G.-J.: Recognition of Leaf Images Based on Shape Features Using a Hypersphere Classifier. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005, Part I. LNCS, vol. 3644, pp. 87–96. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Leslie, E.: Auxin Is Required for Leaf Vein Pattern in Arabidopsis. American Society of Plant Biologists 121(4), 1179–1190 (1999)

    Google Scholar 

  4. Soille, P.: Morphological Image Analysis Applied to Crop Field Mapping. Image and Vision Computing 18, 1025–1032 (2000)

    Article  Google Scholar 

  5. Pramanik, S., Bandyopadhyay, S.K., Bhattacharyya, D., Kim, T.-H.: Identification of Plant Using Leaf Image Analysis. In: Kim, T.-H., Pal, S.K., Grosky, W.I., Pissinou, N., Shih, T.K., Ślęzak, D. (eds.) SIP/MulGraB 2010. CCIS, vol. 123, pp. 291–303. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  6. Bhanu, B., Olivier, D.: Shape Matching of Two-Dimensional Objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 6(2) (March 1984)

    Google Scholar 

  7. Soontom, O., Chen, Y.J.: Integer Fast Fourier Transform. IEEE Transaction on Signal Processing 50(3) (March 2002)

    Google Scholar 

  8. Krishna, S., Indra, G., Sangeeta, G.: SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape. International Journal of Signal Processing, Image Processing and Pattern Recognition 3(4) (December 2010)

    Google Scholar 

  9. Bao, P., Zhang, L., Wu, X.L.: Canny Edge Detection Enhancement by Scale Multiplication. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(9) (2005)

    Google Scholar 

  10. Li, X., Shen, J.: Group Direction Difference Chain Codes for the Representation of the Border. In: Digital and Optical Shape Representation and Pattern Recognition, Orlando, FL, pp. 372–376. SPIE, Bellingham (1988)

    Google Scholar 

  11. Rafael, C.G., Richard, E.W.: Digital Image Processing. Prentice Hall

    Google Scholar 

  12. Milan, S., Vaclav, H., Roger, B.: Image Processing Analysis and Machine Vision. Thomson Learning (2008)

    Google Scholar 

  13. ICL Plant Leaf Images Dataset, http://www.intelengine.cn/English/dataset

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© 2012 Springer-Verlag Berlin Heidelberg

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Yang, LW., Wang, XF. (2012). Leaf Image Recognition Using Fourier Transform Based on Ordered Sequence. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_51

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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