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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 in industry.[1][2] The scope of MV is broad.[2][3][4]

MV is related to, though distinct from, computer vision.[2]

Contents

Applications

The primary uses for machine vision are automatic inspection and industrial robot guidance.[5] Common MV applications include quality assurance, sorting, material handling, robot guidance, and optical gauging.[4]

Methods

Machine vision methods are defined as both the process of defining and creating a MV solution,[6][7] and as the technical process that occurs during the operation of the solution. Here the latter is addressed. As of 2006, there was little standardization in the interfacing and configurations used in MV. This includes user interfaces, interfaces for the integration of multi-component systems and automated data interchange.[8] Nonetheless, the first step in the MV 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.[9][10] MV software packages 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.[11]

Though the vast majority of machine vision applications are still solved using 2 dimensional imaging, machine vision applications utilizing 3D imaging are growing niche within the industry.[12][13]

Imaging

While conventional (2D visible light) imaging is most commonly used in MV, alternatives include imaging various infrared bands,[14] line scan imaging, 3D imaging of surfaces and X-ray imaging.[5] 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.[15] The most commonly used method for 3D imaging is is scanning based triangulation. Other 3D methods used for machine vision are time of flight, grid based and stereoscopic.[12]

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.[16][17] When separated, the connection may be made to specialized intermediate hardware, a frame grabber using either a standardized (Camera Link, CoaXPress) or custom interface.[18][19][20][21] MV implementations also have used digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces.[21][22]

Image processing

After an image is acquired it is processed.[20] Machine vision image processing methods include[further explanation needed]

  • Pixel counting: counts the number of light or dark pixels[citation needed]
  • Thresholding: converts an image with gray tones to simply black and white or using separation based on a grayscale value [23]
  • Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.[24]
  • Blob discovery & manipulation: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. These blobs frequently represent optical targets for machining, robotic capture, or manufacturing failure.[25]
  • Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size.[26]
  • Barcode, Data Matrix and "2D barcode" reading [27]
  • Optical character recognition: automated reading of text such as serial numbers [28]
  • Gauging: measurement of object dimensions (e.g. in pixels, inches or millimeters) [29]
  • Edge detection: finding object edges [30]
  • Neural net processing: weighted and self-training multi-variable decision making [31]
  • Filtering (e.g. morphological filtering)[citation needed]
  • Stitching: Combining of adjacent 2D or 3D images.[citation needed]

Outputs

A common output from machine vision systems is pass/fail decisions.[citation needed] 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.[5] 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.[6][10]

Market

As recently as 2006, one industry consultant reported that MV represented a $1.5 billion market in North America.[32] 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."[3]

As of 2006, experts estimated that MV had been employed in less than 20% of the applications for which it is potentially useful.[33]

See also

  • Machine vision glossary
  • Feature detection (computer vision)

References

  1. ^ Steger, Carsten, Markus Ulrich, and Christian Wiedemann (2008). Machine Vision Algorithms and Applications. Weinheim: Wiley-VCH. p. 1. ISBN 978-3-527-40734-7. Retrieved 2010-11-05. 
  2. ^ a b c 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. 
  3. ^ a b Holton, W. Conard (October 2010). "By Any Other Name". Vision Systems Design 15 (10). ISSN 1089-3709. Retrieved 2013-3-5. 
  4. ^ a b Relf, Christopher G. (2004). Image Acquisition and Processing with LabVIEW 1. CRC Press. ISBN 978-0-8493-1480-3. Retrieved 2010-11-02. 
  5. ^ a b c Turek, Fred D. (June 2011). "Machine Vision Fundamentals, How to Make Robots See". NASA Tech Briefs 35 (6): 60–62. Retrieved 2011-11-29. 
  6. ^ a b West, Perry A Roadmap For Building A Machine Vision System Pages 1-35
  7. ^ Dechow, David (January 2009). "Integration: Making it Work". Vision & Sensors: 16–20. Retrieved 2012-05-12. 
  8. ^ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 709. ISBN 978-3-527-40584-8. Retrieved 2010-11-05. 
  9. ^ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 427. ISBN 978-3-527-40584-8. Retrieved 2010-11-05. 
  10. ^ a b Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. ISBN 3-540-66410-6. [page needed]
  11. ^ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 429. ISBN 978-3-527-40584-8. Retrieved 2010-11-05. 
  12. ^ a b Murray, Charles J (February 2012). "3D Machine Vison Comes into Focus". Design News. Retrieved 2012-05-12. 
  13. ^ Davies, E.R. (2012). Computer and Machine Vision: Theory, Algorithms, Practicalities (4th ed.). Academic Press. pp. 410–411. ISBN 9780123869081. Retrieved 2012-05-13. 
  14. ^ Wilson, Andrew (April 2011). "The Infrared Choice". Vision Systems Design 16 (4): 20–23. Retrieved 2013-3-5. 
  15. ^ West, Perry High Speed, Real-Time Machine Vision CyberOptics, pages 1-38
  16. ^ Belbachir, Ahmed Nabil, ed. (2009). Smart Cameras. Springer. ISBN 978-1-4419-0952-7. [page needed]
  17. ^ Dechow, David (February 2013). "Explore the Fundamentals of Machine Vision: Part 1". Vision Systems Design 18 (2): 14–15. Retrieved 2013-3-5. 
  18. ^ Wilson, Andrew (May 31, 2011). "CoaXPress standard gets camera, frame grabber support". Vision Systems Design. Retrieved 2012-11-28. 
  19. ^ Wilson, Dave (November 12, 2012). "Cameras certified as compliant with CoaXPress standard". Vision Systems Design. Retrieved 2013-3-5. 
  20. ^ a b Davies, E.R. (1996). Machine Vision - Theory Algorithms Practicalities (2nd ed.). Harcourt & Company. ISBN 978-0-12-206092-2. [page needed].
  21. ^ a b 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. 
  22. ^ Wilson, Andrew (December2011). "Product Focus - Looking to the Future of Vision". Vision Systems Design 16 (12). Retrieved 2013-3-5. 
  23. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 96. ISBN 3-540-66410-6. 
  24. ^ Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3
  25. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 95. ISBN 3-540-66410-6. 
  26. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 111. ISBN 3-540-66410-6. 
  27. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 125. ISBN 3-540-66410-6. 
  28. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 132. ISBN 3-540-66410-6. 
  29. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 191. ISBN 3-540-66410-6. 
  30. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. p. 108. ISBN 3-540-66410-6. 
  31. ^ Turek, Fred D. (March 2007). "Introduction to Neural Net Machine Vision". Vision Systems Design 12 (3). Retrieved 2013-3-5. 
  32. ^ Hapgood, Fred (December 15, 2006/January 1, 2007). "Factories of the Future". CIO 20 (6): 46. ISSN 0894-9301. Retrieved 2010-10-28. 
  33. ^ Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 694. ISBN 978-3-527-40584-8. Retrieved 2010-11-05. 

External links

(Sebelumnya) MacfusionMachine-readable data (Berikutnya)