Machine vision

Early Automatix (now part of Microscan) machine vision system Autovision II from 1983 being demonstrated at a trade show. Camera on tripod is pointing down at a light table to produce backlit image shown on screen, which is then subjected to blob extraction.

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is also used in a broader sense by trade shows and trade groups; this broader definition also encompasses products and applications most often associated with image processing.

Definition

Definitions of the term "Machine vision" vary, but all include the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection and robot and process guidance in industry.[1][2][3] This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise.[3][4] Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas.[3]:5[5] The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing.[4] The primary uses for machine vision are automatic inspection and industrial robot/process guidance.[6][7]:6–10[8]

Imaging based automatic inspection

The primary uses for machine vision are imaging-based automatic inspection and robot guidance.;[6][7]:6–10 in this section the former is abbreviated as "automatic inspection". The larger scale process includes defining and creating a solution.[9][10] This section describes the technical process that occurs during the operation of the solution.

Methods and sequence of operation

The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing.[11][12] MV software packages and programs developed in them then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information.[13]

Equipment

The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices.[7]:11–13

Imaging

The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a smart camera or smart sensor.[14][15] When separated, the connection may be made to specialized intermediate hardware, a frame grabber using either a standardized (Camera Link, CoaXPress) or custom interface.[16][17][18][19] MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces.[19][20]

While conventional (2D visible light) imaging is most commonly used in MV, alternatives include hyperspectral imaging, imaging various infrared bands,[21] line scan imaging, 3D imaging of surfaces and X-ray imaging.[6] Key divisions within MV 2D visible light imaging are monochromatic vs. color, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes.[22]

Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry.[23][24] The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product or image during the imaging process. A laser is projected onto the surfaces of an object and viewed from a different angle. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud.[25] Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras.[25] Other 3D methods used for machine vision are time of flight, grid based and stereoscopic.[25][23] One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012.[26][27]

Image processing

After an image is acquired, it is processed.[18] Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target vales to create and communicate "pass/fail" results. Machine vision image processing methods include;

Outputs

A common output from automatic inspection systems is pass/fail decisions.[13] These decisions may in turn trigger mechanisms that reject failed items or sound an alarm. Other common outputs include object position and orientation information from robot guidance systems.[6] Additionally, output types include numerical measurement data, data read from codes and characters, displays of the process or results, stored images, alarms from automated space monitoring MV systems, and process control signals.[9][12] This also includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.[39]

Market

As recently as 2006, one industry consultant reported that MV represented a $1.5 billion market in North America.[40] However, the editor-in-chief of an MV trade magazine asserted that "machine vision is not an industry per se" but rather "the integration of technologies and products that provide services or applications that benefit true industries such as automotive or consumer goods manufacturing, agriculture, and defense."[4]

As of 2006, experts estimated that MV had been employed in less than 20% of the applications for which it is potentially useful.[41]

See also

References

  1. ↑ Steger, Carsten; Markus Ulrich & Christian Wiedemann (2008). Machine Vision Algorithms and Applications. Weinheim: Wiley-VCH. p. 1. ISBN 978-3-527-40734-7. Retrieved 2010-11-05.
  2. ↑ Beyerer, Jürgen; Puente León, Fernando & Frese, Christian (2016). Machine Vision - Automated Visual Inspection: Theory, Practice and Applications. Berlin: Springer. ISBN 978-3-662-47793-9. doi:10.1007/978-3-662-47794-6. Retrieved 2016-10-11.
  3. 1 2 3 Graves, Mark & Bruce G. Batchelor (2003). Machine Vision for the Inspection of Natural Products. Springer. p. 5. ISBN 978-1-85233-525-0. Retrieved 2010-11-02.
  4. 1 2 3 Holton, W. Conard (October 2010). "By Any Other Name". Vision Systems Design. 15 (10). ISSN 1089-3709. Retrieved 2013-03-05.
  5. ↑ Owen-Hill, Alex (July 21, 2016). "Robot Vision vs Computer Vision: What's the Difference?". Robotics Tomorrow.
  6. 1 2 3 4 5 Turek, Fred D. (June 2011). "Machine Vision Fundamentals, How to Make Robots See". NASA Tech Briefs. 35 (6): 60–62. Retrieved 2011-11-29.
  7. 1 2 3 Cognex (2016). "Introduction to Machine Vision" (PDF). Assembly Magazine. Retrieved 9 February 2017.
  8. ↑ Lückenhaus, Maximilian (May 1, 2016). "Machine Vision in IIoT". Quality Magazine.
  9. 1 2 West, Perry A Roadmap For Building A Machine Vision System Pages 1-35
  10. ↑ Dechow, David (January 2009). "Integration: Making it Work". Vision & Sensors: 16–20. Retrieved 2012-05-12.
  11. ↑ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 427. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.
  12. 1 2 Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. ISBN 3-540-66410-6.
  13. 1 2 Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 429. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.
  14. ↑ Belbachir, Ahmed Nabil, ed. (2009). Smart Cameras. Springer. ISBN 978-1-4419-0952-7.
  15. ↑ Dechow, David (February 2013). "Explore the Fundamentals of Machine Vision: Part 1". Vision Systems Design. 18 (2): 14–15. Retrieved 2013-03-05.
  16. ↑ Wilson, Andrew (May 31, 2011). "CoaXPress standard gets camera, frame grabber support". Vision Systems Design. Retrieved 2012-11-28.
  17. ↑ Wilson, Dave (November 12, 2012). "Cameras certified as compliant with CoaXPress standard". Vision Systems Design. Retrieved 2013-03-05.
  18. 1 2 Davies, E.R. (1996). Machine Vision - Theory Algorithms Practicalities (2nd ed.). Harcourt & Company. ISBN 978-0-12-206092-2..
  19. 1 2 Dinev, Petko (March 2008). "Digital or Analog? Selecting the Right Camera for an Application Depends on What the Machine Vision System is Trying to Achieve". Vision & Sensors: 10–14. Retrieved 2012-05-12.
  20. ↑ Wilson, Andrew (December 2011). "Product Focus - Looking to the Future of Vision". Vision Systems Design. 16 (12). Retrieved 2013-03-05.
  21. ↑ Wilson, Andrew (April 2011). "The Infrared Choice". Vision Systems Design. 16 (4): 20–23. Retrieved 2013-03-05.
  22. ↑ West, Perry High Speed, Real-Time Machine Vision CyberOptics, pages 1-38
  23. 1 2 Murray, Charles J (February 2012). "3D Machine Vison Comes into Focus". Design News. Retrieved 2012-05-12.
  24. ↑ Davies, E.R. (2012). Computer and Machine Vision: Theory, Algorithms, Practicalities (4th ed.). Academic Press. pp. 410–411. ISBN 9780123869081. Retrieved 2012-05-13.
  25. 1 2 3 3-D Imaging: A practical Overview for Machine Vision By Fred Turek & Kim Jackson Quality Magazine, March 2014 issue, Volume 53/Number 3 Pages 6-8
  26. ↑ http://research.microsoft.com/en-us/people/fengwu/depth-icip-12.pdf HYBRID STRUCTURED LIGHT FOR SCALABLE DEPTH SENSING Yueyi Zhang, Zhiwei Xiong, Feng Wu University of Science and Technology of China, Hefei, China Microsoft Research Asia, Beijing, China
  27. ↑ R.Morano, C.Ozturk, R.Conn, S.Dubin, S.Zietz, J.Nissano, "Structured light using pseudorandom codes", IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3)(1998)322–327
  28. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 39. ISBN 3-540-66410-6.
  29. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 96. ISBN 3-540-66410-6.
  30. ↑ Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3
  31. ↑ Lauren Barghout. Visual Taxometric approach Image Segmentation using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions. Information Processing and Management of Uncertainty in Knowledge-Based Systems. CCIS Springer-Verlag. 2014
  32. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 108. ISBN 3-540-66410-6.
  33. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 95. ISBN 3-540-66410-6.
  34. ↑ Turek, Fred D. (March 2007). "Introduction to Neural Net Machine Vision". Vision Systems Design. 12 (3). Retrieved 2013-03-05.
  35. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 111. ISBN 3-540-66410-6.
  36. 1 2 Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 125. ISBN 3-540-66410-6.
  37. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 132. ISBN 3-540-66410-6.
  38. ↑ Demant C.; Streicher-Abel B. & Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 191. ISBN 3-540-66410-6.
  39. ↑ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 709. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.
  40. ↑ Hapgood, Fred (December 15, 2006 – January 1, 2007). "Factories of the Future". CIO. 20 (6): 46. ISSN 0894-9301. Retrieved 2010-10-28.
  41. ↑ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 694. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.
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