Skip to main content
SpringerLink
Log in

Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

For apple harvesting robot, it is difficult to acquire the coordinates of occluded apples accurately in natural scenes, which is important in implementing picking tasks. In this paper, a method on automatic recognition and localization of occluded apples was proposed. Firstly, an apple recognition algorithm based on K-means clustering theory was described. Secondly, convex hull information which was obtained by following the contours of extracted apple regions was used to extract the real apple edges. Finally, three points from these real edges were selected to estimate the centers and radius of apples. This algorithm was tested and compared with traditional Hough transform method (HT method) and contour curvature method (CC method) and 125 apple images were used to test the effectiveness of these methods. Four parameters including Segmentation Error (SE), False Positive Rate (FPR), False Negative Rate (FNR) and Overlap Index (OI) were used to evaluate the performance of these methods. Experimental results showed that SE of the presented method was decreased by 14.399 and 30.782 % when compared to CC method and HT method respectively, FPR by 7.234 and 11.728 % and OI was increased by 18.644 and 30.938 %. FNR of the proposed method was 0.912 % lower than CC method, while it was 5.869 % higher than HT method. The experimental results indicated that the proposed method could get much better localization rate than Hough transform method and contour curvature method, thus it could be concluded that the algorithm is an efficient means for the recognition and localization of occluded apples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Bulanon D, Kataoka T, Ota Y, Hiroma T (2002) AE—automation and emerging technologies: a segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosyst Eng 83(4):405–412

    Article  Google Scholar 

  2. Canny J (1986) A computational approach to edge detection. Pattern analysis and machine intelligence. IEEE Trans (6):679–698

  3. Fang C, Ma J (2009) A novel k’-means algorithm for clustering analysis. In: biomedical engineering and informatics. IEEE 1–5

  4. Feng J, Wang S, Liu G, Zeng L (2012) A separating method of adjacent apples based on machine vision and chain code information. In: Computer and Computing Technologies in Agriculture V. Springer. 258–267

  5. Gonzalez R, Woods R, Eddins S (2009) Digital image processing using MATLAB, vol 2. Gatesmark Publishing, Knoxville

    Google Scholar 

  6. Guo B, Zhao J (2004) Discrimination of mature tomato based on his color space in natural outdoor scenes. Trans Chin Soc Agric Mach 35(9):122–124,135

    Google Scholar 

  7. Hannan M, Burks T, Bulanon DM (2010) A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. Agricultural Engineering International: CIGR J

  8. Huang HC, Chu SC, Pan JS, Lu ZM (2001) A tabu search based maximum descent algorithm for VQ codebook design. J Inf Sci Eng 17(5):753–762

    Google Scholar 

  9. Huang HC, Pan JS, Lu ZM, Sun SH, Hang HM (2001) Vector quantization based on genetic simulated annealing. Signal Process 81(7):1513–1523

    Article  MATH  Google Scholar 

  10. Muscato G, Prestifilippo M, Abbate N, Rizzuto I (2005) A prototype of an orange picking robot: past history, the new robot and experimental results. Ind Robot 32(2):128–138

    Article  Google Scholar 

  11. Plebe A, Grasso G (2001) Localization of spherical fruits for robotic harvesting. Mach Vis Appl 13(2):70–79

    Article  Google Scholar 

  12. Rekik A, Zribi M, Benjelloun M, ben Hamida A (2006) A k-means clustering algorithm initialization for unsupervised statistical satellite image segmentation. E-Learning in Industrial Electronics, IEEE, In, pp 11–16

    Google Scholar 

  13. Sarig Y (1993) Robotics of fruit harvesting: a state-of-the-art review. J Agric Eng Res 54(4):265–280

    Article  Google Scholar 

  14. Schwarz MW, Cowan WB, Beatty JC (1987) An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Trans Graph 6(2):123–158

    Article  Google Scholar 

  15. Song H, He D, Pan J (2012) Recognition and localization methods of occluded apples based on convex hull theory. Trans Chin Soc Agric Eng 28(22):174–180

    Google Scholar 

  16. Song H, Zhang W, Zhang X, Zou R (2014) Shadow removal method of apples based on fuzzy set theory. Trans Chin Soc Agric Eng 30(3):135–141

    Google Scholar 

  17. Stajnko D, Čmelik Z (2005) Modelling of apple fruit growth by application of image analysis. Sci Agric Conspec 70(2):59–64

    Google Scholar 

  18. Sun J, Lu ZM, Zhou L (2014) Iris Recognition using curvelet transform based on principal component analysis and linear discriminant analysis. J Inf Hiding Multimedia Signal Process 5(3):567–573

    Google Scholar 

  19. Wang XX, Ma LY (2014) A compact K nearest neighbor classification for power plant fault diagnosis. J Inf Hiding Multimedia Signal Process 5:508–517

    Google Scholar 

  20. Xu H, Ye Z, Ying Y (2005) Identification of citrus fruit in a tree canopy using color information. Trans Chin Soc Agric Eng 5:023

    Google Scholar 

  21. Xun Y, Chen X, Li W, Liu G, Xu C (2008) Automatic recognition of on-tree apples based on contour curvature. J Jiangsu Univ (Nat Sci Ed) 28(6):461–464

    Google Scholar 

  22. Ya wena P, Wan junb L, Wen taoa J (2012) Identification of vehicle with block license plate based on PSO-IFCM. Comput Eng 14:048

    Google Scholar 

  23. Yao H, Duan Q, Li D, Wang J (2013) An improved -means clustering algorithm for fish image segmentation. Math Comput Model 58(3–4):790–798

    Article  MATH  Google Scholar 

  24. Yin H, Chai Y, Yang SX, Mittal GS (2009) Ripe tomato recognition and localization for a tomato harvesting robotic system. Soft Comput Pattern Recog IEEE 557–562

  25. Yin H, Chai Y, Yang S X, Mittal G S (2009) Ripe tomato recognition and localization for a tomato harvesting robotic system In: Soft Comput Pattern Recog IEEE 557–562

  26. Zhao J, Tow J, Katupitiya J (2005) On-tree fruit recognition using texture properties and color data In: Intell Robot Systems IEEE 263–268

  27. Zhou Q, Huang T, Wu H, Li Z, Lin X (2008) A new algorithm for finding convex hull with a maximum pitch of the dynamical base line. In: Knowl Disc Data Min IEEE 630–634

Download references

Acknowledgments

This work is supported by the National High Technology Research and Development Program of China (863 Program) (No.2013AA10230402), “National Natural Science Foundation” of China (No. 31000670), and the “Fundamental Research Funds for the Central Universities” of China (No. QN2011031). The authors would like to thank Shaojin Wang (Ph.D, College of Mechanical and Electronic Engineering, Northwest A&F University), Xiuli Yu (graduate student, College of Mechanical and Electronic Engineering, Northwest A&F University) and Weifeng Qu graduate student, College of Mechanical and Electronic Engineering, Northwest A&F University) for their useful advices.

Author information

Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China

    Dandan Wang, Huaibo Song, Zhihui Tie, Weiyuan Zhang & Dongjian He

Authors
  1. Dandan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huaibo Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihui Tie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongjian He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaibo Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, D., Song, H., Tie, Z. et al. Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison. Multimed Tools Appl 75, 3177–3198 (2016). https://doi.org/10.1007/s11042-014-2429-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2429-9

Keywords

Search

Navigation