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Machine vision (MV) is the process of applying a range of technologies and methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications. While the scope of MV is broad and a comprehensive definition is difficult to distil, a "generally accepted definition of machine vision is '... the analysis of images to extract data for controlling a process or activity.'"

Applications

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

Methods

Machine vision methods are defined as both the process of defining and creating a MV solution, 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. 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. MV software packages then employ various digital image processing techniques to allow the hardware to recognize what it is looking at, extract the required information, and often make decisions (such as pass/fail) based on the extracted information.

Imaging

While conventional (2D visible light) imaging is most commonly used in MV, alternatives include imaging various infrared bands, line scan imaging, 3D imaging of surfaces and X-ray imaging. 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.

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

Image processing

Techniques used in MV image processing include: thresholding (converting a grayscale image to black and white, or using separation based on a grayscale value), segmentation, blob extraction, pattern recognition, barcode and data matrix code reading, optical character recognition, gauging (measuring object dimensions), positioning, edge detection, color analysis, filtering (e.g. morphological filtering) and template matching (finding, matching, and/or counting specific patterns).

Outputs

The most common outputs from machine vision systems are pass/fail decisions. 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. 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.

3D Machine vision

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3D machine vision is used on a small but growing fraction of applications. Several techniques are used to derive 3D information. Each has both significant limitations in its 3D capabilities, application range, and provides less information on the 3D surfaces than 2D machine vision, so 3D is usually applied where it is difficult or impossible to accomplish the application using 2D machine vision. The most mature and widely used technique is 3D laser-based triangulation. Other methods include time-of-flight, IR laser grid based and stereoscopic.

Other techniques (sometimes called "2-1/2D") derive a more limited amount of 3D information from 2D images. These use a wide range of methods such as structured lighting, and computational methods that are based on known object sizes.

Market

This article needs to be updated. Please help update this section to reflect recent events or newly available information. (November 2011)

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

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

See also

• Computer Vision
• Industrial robot
• Machine vision glossary
• Morphological image processing
• OpenCV

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.{{cite book}}: CS1 maint: multiple names: authors list (link)
2. ^ 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. ^ Holton, W. Conard (October 1, 2010). "By Any Other Name". Vision Systems Design. 15 (10). ISSN 1089-3709. Retrieved 2010-10-29.
4. ^ Relf, Christopher G. (2004). Image Acquisition and Processing with LabVIEW. Vol. 1. CRC Press. ISBN 978-0-8493-1480-3. Retrieved 2010-11-02.
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.
6. ^ West, Perry A Roadmap For Building A Machine Vision System Pages 1-35
7. Dechow, David Integration: Making it Work, Vision & Sensors magazine, January 2009, pp 16-20
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. ^ Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. ISBN 3-540-66410-6.
11. Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 429. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.
12. Wilson, Andrew (editor) The Infrared Choice, Vision Systems Design Magazine, April 2011, pages 20-23
13. West, Perry High Speed, Real-Time Machine Vision CyberOptics, pages 1-38
14. ^ , Davies, E.R., Machine Vision - Theory Algorithms Practicalities 2nd Edition Academic Press, Harcourt & Company, Publishers ISBN 0-12-206092-X
15. ^ 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 Magazine: 10–14.
16. Demant C., Streicher-Abel B. and Waszkewitz P. (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer-Verlag. pp. 95, 96, 108, 111, 125, 132, 191. ISBN 3-540-66410-6.
17. Hapgood, Fred (December 15, 2006/January 1, 2007). "Factories of the Future". CIO. 20 (6): 46. ISSN 0894-9301. Retrieved 2010-10-28. {{cite journal}}: Check date values in: |date= (help)
18. Hornberg, Alexander (2006). Handbook of Machine Vision. Wiley-VCH. p. 694. ISBN 978-3-527-40584-8. Retrieved 2010-11-05.

Further reading

• Davies, E. Roy (2005). Machine Vision: Theory, Algorithms, Practicalities (3rd ed.). Amsterdam, Boston: Elsevier. ISBN 978-0-12-206093-9.

External links

• Template:Dmoz

• Applications of computer vision
• Computer vision