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Multimedia Tools and Applications

, Volume 76, Issue 12, pp 14075–14089 | Cite as

Extracting the symmetry axes of partially occluded single apples in natural scene using convex hull theory and shape context algorithm

  • Leilei Niu
  • Weicong Zhou
  • Dandan Wang
  • Dongjian He
  • Haihui Zhang
  • Huaibo SongEmail author
Article
  • 229 Downloads

Abstract

Accurate identification of apples partially occluded by branches and leaves is an urgent and key issue for a picking robot. The objective of this study was to detect the symmetry axes of partially occluded single apples accurately using the convex hull theory and Shape Context algorithm. Firstly, apple regions were obtained by using K-means clustering algorithm. Secondly, image pre-processing steps such as image binarization, hole filling, area opening and edge detection were applied. Thirdly, false contours were removed based on the convex hull theory to enhance the accuracy and stability of this method. Finally, the point matching relationship of each two contours and the two best symmetrical contours were found by using the Shape Context algorithm and Hungarian algorithm. Then the symmetry axes of apples were extracted using the matching point pairs. Least squares ellipses fitting algorithm and moment of inertia algorithm were used to compare with the presented algorithm. The angle difference between extracted symmetry axis and ideal symmetry axis for every method was computed, and the execution time of program as well. Ninety partially occluded single apple images were tested. The experimental results showed that the average angle error of the Shape Context algorithm were 7.72°, 37.5 % of the ellipses fitting algorithm and 31.3 % of the inertia moment algorithm. And its average execution time is 1.86 s, 103 % of the ellipses fitting algorithm and 106 % of the inertia moment algorithm. In conclusion, it was feasible to use the proposed method to extract the symmetry axes of partially occluded apples.

Keywords

Occluded apple Symmetric axis extraction Shape context Convex hull theory K-means clustering algorithm Threshold method 

Notes

Acknowledgments

This work is supported by the National High Technology Research and Development Program of China (863 Program) (No.2013AA10230402), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2014JQ3094). The authors would like to thank all the authors cited in this article and anonymous referees for their helpful comments and suggestions.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Leilei Niu
    • 1
  • Weicong Zhou
    • 1
  • Dandan Wang
    • 1
  • Dongjian He
    • 1
  • Haihui Zhang
    • 1
  • Huaibo Song
    • 1
    Email author
  1. 1.College of Mechanical and Electronic EngineeringNorthwest A&F UniversityYanglingChina

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