Skip to main content

Advertisement

Springer Nature Link
Log in

A vision-based intelligent inspector for wine production

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

A machine-vision-based intelligent inspector is presented. The mechanical structure and electric control system are illustrated in detail. The sub-pixel edge location method is used for confirming the inspection region. The second-difference and energy accumulation method are used for identifying the small moving objects. The algorithms of shape recognition and moving trajectory discrimination are used to extract the foreign substances. A prototype was developed and experimental results demonstrate the feasibility of the inspector. Inspections performed by the prototype have proved the effectiveness and value of proposed algorithms in automatic real-time inspection.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Xin G (2007) Foreign bodies are frequent: the wine of “Lao Cunzhang” annoy consumers. Consumers’ Rights, China 2:22–24

    Google Scholar 

  2. Li Z (2006) Foreign substances were found in snow beer. Commodity and Quality, China 370(6):23

    Google Scholar 

  3. Zhang Y, Xiao W (2008) Analysis of commercial liquor sampling. Heihe Sci Technol 1:41–42

    Google Scholar 

  4. Li SZ (2008) Study of crystal precipitation in liquor. Liquor mak 35(5):31–32

    Google Scholar 

  5. Duan A, Gan Y (2003) Quality control on the filter. Beer Sci Technol 8:38

    Google Scholar 

  6. Huang X (2006) The technology of improving the quality of bottled liquor packaging. Liquor Sci Technol 143(5):65–66

    Google Scholar 

  7. Liu M, Ning Y, Xu G (2005) Quality control during the packaging of bottled beer. China Brewing 11:42–44

    Google Scholar 

  8. Huanjun L, Yaonan W, Feng D (2006) An empty bottle intelligent inspector ased on support vector machines and fuzzy theory. Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21–23, Dalian, China

  9. Feng D, YaoNan W, HuanJun L (2007) A machine vision inspector for beer bottle. Eng Appl Artif Intell 20:1013–1021

    Article  Google Scholar 

  10. Zhang H, Wang Y, Zhou B et al (2009) Development of healthy wine visible particle detection system based on machine vision. Chinese J Sci Instrum 30(5):973–979

    Google Scholar 

  11. Li Y, Wang Y, Wang W (2006) Intelligent transfusion liquor inspector based on machine-vision. Opto-Electron Eng 33(11):69–74

    Google Scholar 

  12. Juan Lu, Yao-nan Wang, Jie Zhang (2008) On-line detection of foreign substances in glass bottles filled with transfusion solution through computer vision. Proceedings of the 2008 IEEE International Conference on Information and Automation June 20–23, Zhangjiajie, China

  13. Karathanassi, Chr. Iossifidis, D. (1996) Application of machine vision techniques in the quality control of pharmaceutical solutions.V Rokos. Comp Ind 32:169–179

    Google Scholar 

  14. Akira Ishii, Takayuki Mizuta, Shigehiko Todo (1998) Detection of foreign substances mixed in a plastic bottle of medicinal solution using real-time video image processing. ICPR.1998(Vol II: 1646–1650)

  15. Zhang Shumei, McCullagh Paul, Nugent Chris, Zheng Huiru, Baumgarten Matthias (2011) Optimal model selection for posture recognition in home-based healthcare. Int J Mach Learn Cybern 1(2):1–14

    Article  Google Scholar 

  16. Jie Li, Han Guan, Wen Jing, Gao Xinbo (2011) Robust tensor subspace learning for anomaly detection. Int J Mach Learn Cybern 2:89–98

    Article  Google Scholar 

Download references

Acknowledgments

The authors appreciate the close cooperation of Hunan Chinsun Pharmaceutical Machinery CO., LTD. for its technical support and China Jing Brand CO., LTD. for the assistance in experiment sample collection. The authors also thank Mr. Guohua Wang and Yan Liu for the experiment help. This work is supported by National High Technology Research and Development Program of China (2007AA04Z244, 2008AA04Z214) and Major Program of National Natural Science Foundation of China (60835004, 60775047).

Author information

Authors and Affiliations

  1. Department of Electronic and Information Engineering, Hunan University, Changsha, 410082, China

    Yaonan Wang, Bowen Zhou, Hui Zhang & Ji Ge

Authors
  1. Yaonan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bowen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ji Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bowen Zhou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Y., Zhou, B., Zhang, H. et al. A vision-based intelligent inspector for wine production. Int. J. Mach. Learn. & Cyber. 3, 193–203 (2012). https://doi.org/10.1007/s13042-011-0051-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-011-0051-y

Keywords

Search

Navigation