Skip to main content
SpringerLink
Log in

Machine Vision

  • Chapter
  • First Online:
Environmental Perception Technology for Unmanned Systems

Part of the book series: Unmanned System Technologies ((UST))

Abstract

Machine vision means to endow the machine with visual perception system which provides similar biological visual ability to facilitate situational awareness. The machine vision system converts the ingested object into an image signal by an image capturing device, transmits it to a dedicated image processing system and converts it into a digital signal according to the pixel distribution, width, color, etc. The image system performs various computations on the signals to extract the target characteristics. By discerning the results, the site operations are being controlled. The main research goal of machine vision is to enable computers to have the ability to recognize three-dimensional environmental information through two-dimensional images, and to sense and process geometric information such as shape, position, posture, and motion of the objects in a three-dimensional environment.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  1. Chen H (2009) Classification of universal cameras. Telev Technol 33(5):127–128

    Google Scholar 

  2. Gao X, Zhang T (2017) Visual SLAM XIV: from theory to practice. Electronics Industry Press, p 3

    Google Scholar 

  3. Gao Z, Zhang Y, Sun P et al (2017) Review of research on unmanned ships. J Dalian Marit Univ 43(2):1–7

    Google Scholar 

  4. Zeng W (2013) Research on water surface target detection and tracking of unmanned boats based on optical vision. Harbin Engineering University

    Google Scholar 

  5. Zhang Y (2015) Research on visual target image recognition technology for surface unmanned boats. Harbin Engineering University

    Google Scholar 

  6. Winner H, Hakuli S, Lotz F et al (2015) Handbook of driver assistance systems

    Google Scholar 

  7. Bay H, Ess A, Tuytelaars T et al (2008) Speeded-up robust features. Comput Vis Image Underst 110(3):404–417

    Article  Google Scholar 

  8. Jia Y (2000) Machine vision. Science Press, p 4

    Google Scholar 

  9. Zhang J (2006) Research on image acquisition and processing system based on machine vision. Chengdu University of Technology

    Google Scholar 

  10. Zhang Y, Wu X, Ma T et al (2006) Research on image acquisition and recognition of parts based on machine vision. Inf Res 32(4):29–31

    Google Scholar 

  11. Ma J (2017) Vision-based unmanned aerial vehicle target recognition and tracking control. Dalian Maritime University

    Google Scholar 

  12. Wang D (2016) Research on vision-based UAV detection and tracking system. Harbin Institute of Technology

    Google Scholar 

  13. Li X (2017) Research on target tracking and localization algorithm of drone based on computer vision. Yanshan University

    Google Scholar 

  14. Ding M (2006) Research on autonomous landing method of drone based on computer vision. Nanjing University of Aeronautics and Astronautics

    Google Scholar 

  15. Yang F (2008) Vision-based research and implementation of UAV autonomous landing system. Tsinghua University

    Google Scholar 

  16. Huang Z (2018) Autonomous landing system of UAV based on machine vision. China Strategic Emerging Industries, p 4

    Google Scholar 

  17. Zou Y (2016) Research on autonomous landing technology of drone based on machine vision. Jiangsu University

    Google Scholar 

  18. He J, Li B (2018) Research on autonomous landing system of UAV based on machine vision. Science and Technology Innovation, p 11

    Google Scholar 

  19. Shilin婕 (2015) Research on UAV visual aid landing algorithm. Xidian University

    Google Scholar 

  20. Li G (2016) Research on UAV power line inspection technology based on machine vision. Anhui University of Science and Technology

    Google Scholar 

  21. 胥鹏 (2017) A machine vision based unmanned aerial vehicle obstacle avoidance system, CN205910595U[P]

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tongji University, Shanghai, China

    Xin Bi

Authors
  1. Xin Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Bi .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Huazhong University of Science and Technology Press and Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bi, X. (2021). Machine Vision. In: Environmental Perception Technology for Unmanned Systems. Unmanned System Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-8093-2_4

Download citation

Publish with us

Policies and ethics

Search

Navigation