Leaf Segmentation, Its 3D Position Estimation and Leaf Classification from a Few Images with Very Close Viewpoints

  • Chin-Hung Teng
  • Yi-Ting Kuo
  • Yung-Sheng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5627)

Abstract

In this paper, we present a complete system to extract leaves, recover their 3D positions and finally classify them based on leaf shape. We use only a few images with slightly different viewpoints to achieve the task. The images are captured by a general hand-held digital camera and no camera pre-calibration is required. Because only a few images with close viewpoints are sufficient to segment the leaves and recover their 3D positions, our system is flexible and easy to use in image acquisition. For leaf classification, we use the normalized centroid-contour distance as our classification feature and employ a circular-shift comparing scheme to measure the similarity, thus our system has the advantages of being invariant to leaf translation, rotation and scaling. We have conducted several experiments and the results are encouraging. The leaves are nearly perfectly extracted and the classification results are also acceptable.

Keywords

Leaf Segmentation Leaf 3D Recovery Leaf Classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wang, Z., Chi, Z., Feng, D.: Shape based leaf image retrieval. In: IEE Proceedings on Vision, Image and Signal Process, vol. 150, pp. 34–43 (2003)Google Scholar
  2. 2.
    Shen, Y., Zhou, C., Lin, K.: Leaf image retrieval using a shape based method. IFIP International Federation for Information Processing – Artificial Intelligence Applications and Innovations 187, 711–719 (2005)CrossRefGoogle Scholar
  3. 3.
    Saitoh, T., Kaneko, T.: Automatic recognition of wild flowers. Systems and Computers in Japan 34(10), 90–101 (2003)CrossRefGoogle Scholar
  4. 4.
    Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1447–1454 (2006)Google Scholar
  5. 5.
    Nilsback, M.E., Zisserman, A.: Delving into the whorl of flower segmentation. In: Proceedings of British Machine Vision Conference (2007)Google Scholar
  6. 6.
    Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of Computer Vision, Graphics and Image Processing in Indian, pp. 722–729 (2008)Google Scholar
  7. 7.
    Sclaroff, S., Liu, L.: Deformable shape detection and description via model-based region grouping. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(5), 475–489 (2001)CrossRefGoogle Scholar
  8. 8.
    Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. ACM Transactions on Graphics 23(3), 303–308 (2004)CrossRefGoogle Scholar
  9. 9.
    Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., Kang, S.B.: Image-based plant modeling. ACM Transactions on Graphics 25(3), 599–604 (2006)CrossRefGoogle Scholar
  10. 10.
    Teng, C.H., Chen, Y.S., Hsu, W.H.: Camera self-calibration method suitable for variant camera constraints. Applied Optics 45(4), 688–696 (2006)CrossRefGoogle Scholar
  11. 11.
    Teng, C.H., Lai, S.H., Chen, Y.S., Hsu, W.H.: Accurate optical flow computation under non-uniform brightness variations. Computer Vision and Image Understanding 97, 315–346 (2005)CrossRefGoogle Scholar
  12. 12.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  13. 13.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  14. 14.
    Boykov, Y., Kolomogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chin-Hung Teng
    • 1
  • Yi-Ting Kuo
    • 2
  • Yung-Sheng Chen
    • 2
  1. 1.Department of Information CommunicationYuan Ze UniversityTaiwan
  2. 2.Department of Electrical EngineeringYuan Ze UniversityTaiwan

Personalised recommendations