Skip to main content

Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

  • Chapter
Integrated Imaging and Vision Techniques for Industrial Inspection

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a better performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.

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

Access this chapter

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

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

Similar content being viewed by others

References

  1. Dowling KJ, Mueller GG, Lys IA (2003) Systems and methods for providing illumination in machine vision systems. Patent: US 6624597B2. United States, Boston, MA, US: Assignee: Color Kinetics, Inc

    Google Scholar 

  2. Newman TS, Jain AK (1995) A survey of automated visual inspection. Comput Vis Image Underst 61(2):231–262. doi:10.1006/cviu.1995.1017

    Article  Google Scholar 

  3. Batchelor BG (1999) Coming to terms with machine vision and computer vision. Advanced imaging, pp. 22–26

    Google Scholar 

  4. Labudzki R, Legutko S (2011) Applications of machine vision. Manufact Ind Eng 2:27–29

    Google Scholar 

  5. Malamas EN, Petrakis EG, Zervakis M, Petit L, Legat JD (2003) A survey on industrial vision systems, applications and tools. Image Vis Comput 21(2):171–188. doi:10.1016/S0262-8856(02)00152-X

    Article  Google Scholar 

  6. Thomas A, Rodd M, Holt J, Neill C (1995) Real-time industrial visual inspection: a review. Real-Time Imaging 1(2):139–158. doi:10.1006/rtim.1995.1014

    Article  Google Scholar 

  7. Gregory R Planning a pc-based machine vision system. Online white paper. URL http://archives.sensorsmag.com/articles/0498/vis0498/index.htm. Accessed in March 2015

  8. Bier J (2011) Implementing vision capabilities in embedded systems. Online white paper (2011). URL http://www.embedded-vision.com. Accessed in March 2015

  9. Gardner JS Challenges to embedding computer vision. Online. URL http://www.embedded-vision.com. Accessed in March 2015

  10. Berkeley Design Technology Inc (2011) Implementing vision capabilities in embedded systems. Presentation at 2011 Embedded Systems Conference Silicon Valley

    Google Scholar 

  11. Dipert B, Khan K (2013) The embedded vision revolution. Online article (2013). URL http://www.mddionline.com/article/embedded-vision-revolution. Accessed in March 2015

  12. Wolf W, Ozer B, Lv T (2002) Smart cameras as embedded systems. Computer 35(9):48–53. doi:10.1109/MC.2002.1033027

    Article  Google Scholar 

  13. Shi D, Lichman S (2005) Smart cameras: a review. In: Proceedings of 2005 Asia-Pacific workshop on visual information processing. Hong Kong, China, pp 11–13

    Google Scholar 

  14. de Sousa A (2003) Smart cameras as embedded systems. In: Proceedings first international conference on computer applications, pp. 105–112. Session 4: Embedded Systems

    Google Scholar 

  15. Shi Y, Real F (2010) Smart cameras: fundamentals and classification. In: Belbachir AN (ed) Smart cameras. Springer, US, pp 19–34. doi:10.1007/978-1-4419-0953-4-2

  16. Wilson A (2013) Time-of-flight camera captures VGA images at high speed. Vis Syst Des 18(1):11–12

    Google Scholar 

  17. Malik AW, Thoornberg B, Kumar P (2013) Comparison of three smart camera architectures for real-time machine vision system. Int J Adv Rob Syst 10:1–12. doi:10.5772/57135 10:402

    Article  Google Scholar 

  18. Elouardi A, Bouaziz S, Dupret A, Lacassagne L, Klein J, Reynaud R (2006) A smart sensor for image processing: towards a system on chip. In: IEEE International Symposium on Industrial Electronics, vol 4, pp. 2857–2862. doi 10.1109/ISIE.2006.296069

  19. Rodriguez-Vazquez A, Dominguez-Castro R, Jimenez-Garrido F, Morillas S (2010) A CMOS vision system on-chip with multicore sensory processing architecture for image analysis above 1000 f/s. In: Bodegom E, Nguyen V (eds) Proceedings of SPIE, sensors, cameras, and systems for industrial/scientific applications XI, vol 7536, pp. 75,3600–75,3600–11. San Jose, California, USA. doi:10.1117/12.839183

  20. Martin D (2007) A practical guide to machine vision lighting. Online white paper. URL http://www.graftek.com/. Accessed in March 2015

  21. Hecht K (2005) Integrating LED illumination system for machine vision systems. Patent: US6871993B2, Hatfield, PA, US. Assignee: Accu-Sort Systems, Inc. 2006

    Google Scholar 

  22. Gardasoft The practical use of LED light controllers within machine vision systems. Online white paper. URL http://www.gardasoft.com. Accessed in March 2015

  23. Wang W, Li W (2009) Design of reconfigurable LED illumination control system based on fpga. In: Image and Signal Processing, CISP ’09. 2nd International Congress on, pp. 1–4. doi:10.1109/CISP.2009.5304361

  24. MICROSCAN Eight tips for optimal machine vision lighting. Online technology white paper. URL http://www.microscan.com. Accessed in March 2015

  25. Dowling KJ, Mueller GG, Lys IA (2006) Systems and methods for providing illumination in machine vision systems. Patent: US7042172B2, United States, Boston, MA, US. Assignee: Color Kinetics Inc

    Google Scholar 

  26. Dechow D (2013) Explore the fundamentals of machine vision: part I. Vision Sys Des 18(2):14–15

    Google Scholar 

  27. Teledyne Dalsa: Application notes and technology primer: CCD versus CMOS. Online article. URL https://www.teledynedalsa.com. Accessed in March 2015

  28. Apler G (2011) CCD versus CMOS image sensors: the lines are blurring. Online article (2011). URL http://info.adimec.com/. Accessed in March 2015

  29. Bosiers JT, Peters IM, Draijer C, Theuwissen A (2006) Technical challenges and recent progress in (CCD) imagers. Nucl Instrum Methods Phys Res Sect. A Accelerators, Spectrometers, Detectors and Associated Equipment 565(1):148–156. doi http://dx.doi.org/10.1016/j.nima.2006.05.033. Proceedings of the International Workshop on Semiconductor Pixel Detectors for Particles and Imaging (PIXEL) 2005 International Workshop on Semiconductor Pixel Detectors for Particles and Imaging

  30. Teledyna Dalsa: X-ray imaging: emerging digital technology—CMOS detectors. Online white paper. URL https://www.teledynedalsa.com/imaging/products/x-ray/. Accessed in March 2015

  31. Korthout L, Verbugt D, Timpert J, Mierop A, de Haan W, Maes W, de Meulmeester J, Muhammad W, Dillen B, Stoldt H et al (2009) A wafer-scale CMOS APS imager for medical X-ray applications. In: International Image Sensor Workshop. Bergen, Norway

    Google Scholar 

  32. Princeton Instruments Imaging Group: Introduction to scientific InGaAs FPA cameras. Oneline technical note (2012). URL http://www.princetoninstruments.com/. Accessed in March 2013

  33. Barton JB, Cannata RF, Petronio SM (2002) InGaAs NIR focal plane arrays for imaging and DWDM applications. In: AeroSense 2002, International society for optics and photonics, pp. 37–47

    Google Scholar 

  34. Hamamatsu Photonics High sensitivity cameras: principle and technology. Online technical note. URL http://www.hamamatsu.com. Accessed in March 2015

  35. Matsumoto M, Mitani K, Sugimoto M, Hashimoto K, Miller R (2012) Innovative bridge assessment methods using image processing and infrared thermography technology. IABSE congress report 18(13):1181–1188. URL http://www.ingentaconnect.com/content/iabse/congr/2012/00000018/00000013/art00004

  36. Munro JF (2008) Systems for capturing three-dimensional images and methods thereof. Patent: US20080050013A1, United States, Rochester, NY, USA

    Google Scholar 

  37. von Fintel R Comparison of the most common digital interface technologies in vision technology: camera link, USB3 vision, GigE vision, FireWire. Online white paper. URL http://www.baslerweb.com. Accessed in March 2015

  38. Edmund Optics Imaging electronics 101: camera types and interfaces for machine vision applications. Online white paper. URL http://www.edmundoptics.com/technical-resources-center/imaging/camera-types-and-interfaces-for-machine-vision-applications. Accessed in March 2015

  39. Automated Imaging Association Gige vision—true plug and play connectivity. Online article. URL http://www.visiononline.org. Accessed in March 2015

  40. Automated Imaging Association Camera link the only real-time machine vision protocol. Online article. URL http://www.visiononline.org. Accessed in March 2015

  41. Association U.I.V CoaXPress high speed camera interface. Online white paper. URL www.ukiva.org. Accessed in March 2015

  42. Adimec (2013) Multi-camera vision system with CoaXPress. Online white paper (2013). Accessed in March 2015

    Google Scholar 

  43. Matrox GPU processing using MIL. Online white paper. URL http://www.matrox.com. Accessed in March 2015

  44. Tweddle B (2009) Graphics processing unit (GPU) acceleration of machine vision software for space flight applications. Workshop on space flight software. URL http://flightsoftware.jhuapl.edu. Presentation Slide

  45. MVTec (2011) MVTec improves factory throughput and quality using NVIDIA GPU-accelerated inspection automation. Online Article. URL http://www.mvtec.com. Accessed in March 2015

  46. Larson B (2015) GPU-accelerated machine vision. Camera and photos 21. URL http://www.objc.io/issue-21/gpu-accelerated-machine-vision.html

  47. Dipert B, Alvarez J, Touriguian M (2012) Embedded vision: FPGAs’ next notable technology opportunity. Xcell J 78:14–19

    Google Scholar 

  48. Chen YC, Wang YT (2008) Development of a low-cost machine vision system and its application on inspection processes. Tamkang J Sci Eng 11(4):425–431

    Google Scholar 

  49. Besiris D, Tsagaris V, Fragoulis N, Theoharatos C (2012) An FPGA-based hardware implementation of configurable pixel-level color image fusion. IEEE Trans Geosci Remote Sens 50(2):362–373. doi:10.1109/TGRS.2011.2163723

    Article  Google Scholar 

  50. Baumgartner D, Roessler P, Kubinger W, Zinner C, Ambrosch K (2009) Benchmarks of low-level vision algorithms for DSP, FPGA, and mobile PC processors. In: Kisacanin B, Bhattacharyya S, Chai S (eds) Embedded computer vision, advances in pattern recognition. Springer, London, pp. 101–120. doi:10.1007/978-1-84800-304-0-5

  51. Dechow D (2013) Explore the fundamentals of machine vision: part II. Vision Sys Des 18(4):16–18

    Google Scholar 

  52. Hornberg A (ed) (2006) Handbook of machine vision. 978-3-527-40584-8. Wiley-VCH, Favoritenstrasse 9/4th Floor/1863

    Google Scholar 

  53. Batchelor BG, Whelan PF (1997) Intelligent vision systems for industry. 3540199691. Springer, London

    Google Scholar 

  54. Jain R, Kasturi R, Schunck BG (1995) Machine vision. No. 0-07-032018-7 in McGraw-Hill Series in computer science. McGraw-Hill, Inc., New York, USA

    Google Scholar 

  55. Pernkopf F (2005) 3D surface acquisition and reconstruction for inspection of raw steel products. Comput Ind 56(89):876–885. doi:http://dx.doi.org/10.1016/j.compind.2005.05.025. (Machine Vision Special Issue)

  56. Pernkopf F (2004) Detection of surface defects on raw steel blocks using bayesian network classifiers. Pattern Anal Appl 7(3):333–342. doi:10.1007/BF02683998

    Article  MathSciNet  Google Scholar 

  57. Liu Z, Genest M, Marincak A, Forsyth D (2008) Characterization of surface deformation with the edge of lighttm technique. Mach Vis Appl 19(1):35–42. doi:10.1007/s00138-007-0075-1

    Article  Google Scholar 

  58. Liu Z, Genest M, Forsyth D, Marincak A (2009) Quantifying surface deformation with the Edge of Light enhanced visual inspection. Instrum Measur IEEE Transactions on 58(2):416–422. doi:10.1109/TIM.2008.2003312

    Article  Google Scholar 

  59. Lillywhite K, Lee DJ, Tippetts B, Archibald J (2013) A feature construction method for general object recognition. Pattern Recogn 46(12):3300–3314. doi:10.1016/j.patcog.2013.06.002

    Article  Google Scholar 

  60. Motoda H, Liu H (2002) Feature selection, extraction and construction. Commun Inst Inf Comput Mach 5:67–72

    Google Scholar 

  61. Wikipedia: Convolutional neural network. URL http://en.wikipedia.org/wiki/Convolutional_neural_network. Accessed in March 2015

  62. Theano Development Team: Deeplearning 0.1 document: Convolutional neural networks (lenet). Online document. URL http://deeplearning.net/tutorial/lenet.html. Accessed in March 2015

  63. Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649

    Google Scholar 

  64. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS Proceedings of advances in neural information processing systems, pp. 1097–1105

    Google Scholar 

  65. OpenCV: Open source computer vision. Website: URL http://opencv.org/

  66. SimpleCV (2013) Computer vision platform using Python. Website: URL http://simplecv.org/. Accessed in March 2015

  67. Kitware Inc. (2014) Visualization toolkit. URL http://www.vtk.org/

  68. Gould S (2012) Darwin: A framework for machine learning and computer vision research and development. J Mach Learn Res 13:3533–3537

    MathSciNet  Google Scholar 

  69. Grigorescu SM, Ristic-Durrant D, Graser A. (2009) ROVIS: Robust machine vision for service robotic system FRIEND. In: Intelligent robots and systems IROS 2009. IEEE/RSJ International Conference on pp. 3574–3581. doi:10.1109/IROS.2009.5354596

  70. Kaehler A, Bradski G (2013) Learning OpenCV: computer vision in C++ with the OpenCV Library, 1st edn. 978-0-596-51613-0. O’Reilly Media, Sebastopol, CA, USA

    Google Scholar 

  71. Demaagd K, Oliver A, Oostendorp N, Scott K (2012) Practical computer vision with SimpleCV, 1st edn. 978-1-449-32036-2. O’Reilly Media, Sebastopol, CA, USA

    Google Scholar 

  72. Schroeder WJ, Martin K, Lorensen W (2003) The visualization toolkit: an object-oriented approach to 3D graphics, 3rd edn. Kitware, Inc. (formerly Prentice-Hall), USA

    Google Scholar 

  73. Schroeder WJ, Martin K, Lorensen WE (1996) The design and implementation of an object-oriented toolkit for 3D graphics and visualization. In: Proceedings of the 7th conference on visualization ’96, VIS ’96. IEEE Computer Society Press, Los Alamitos, CA, USA, pp 93–111. URL http://dl.acm.org/citation.cfm?id=244979.245018

  74. Eledath J (2013) Tools for democratizing computer vision: automated performance characterization. Embedded Vision Summit East Presentations. Westford, MA, USA

    Google Scholar 

  75. Solid State Division Characteristics and use of infrared detectors. Online. URL http://www.hamamatsu.com. Retrieved in March 2015

  76. Maldague XPV (2001) Theory and practice of infrared technology for nondestructive testing. wiley series in microwave and optical engineering. Wiley, USA

    Google Scholar 

  77. Rybicki GB, Lightman AP (1979) Radiative Processes in Astrophysics. Wiley, New York

    Google Scholar 

  78. Blum RS, Liu Z (eds) (2005) Multi-sensor image fusion and its applications. signal processing and communications. Taylor and Francis, UK

    Google Scholar 

  79. Rogalski A (2002) Infrared detectors: an overview. Infrared Phys Technol 43(35):187–210. doi:10.1016/S1350-4495(02)00140-8

    Article  Google Scholar 

  80. Tech note (2015) IR lighting (NIR—near infrared). Online (2015). URL http://smartvisionlights.com

  81. Vadivambal R, Jayas D (2011) Applications of thermal imaging in agriculture and food industrya review. Food Bioprocess Technol 4(2):186–199. doi:10.1007/s11947-010-0333-5

    Article  Google Scholar 

  82. Bagavathiappan S, Lahiri B, Saravanan T, Philip J, Jayakumar T (2013) Infrared thermography for condition monitoring a review. Infrared Phys Technol 60:35–55. doi:10.1016/j.infrared.2013.03.006

    Article  Google Scholar 

  83. Pinter M (2015) Advance in UV light for machine vision applications. Online (2015). URL http://smartvisionlights.com

  84. Richards A (2006) UV imaging opens new applications. Vision Systems Design

    Google Scholar 

  85. Wilson A (2012) Enhanced cameras detect targets in the UV spectrum. Vis Syst Des 17(10):13–14

    Google Scholar 

  86. Slaughter D, Obenland D, Thompson J, Arpaia M, Margosan D (2008) Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence. Postharvest Biol Technol 48(3):341–346. doi:10.1016/j.postharvbio.2007.09.012

    Article  Google Scholar 

  87. Kondo N, Kuramoto M, Shimizu H, Ogawa Y, Kurita M, Nishizu T, Chong VK, Yamamoto K (2009) Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Eng Agric Environ Food 2(2):54–59. doi:10.1016/S1881-8366(09)80016-5

    Article  Google Scholar 

  88. Adams T (2000) What happened here: diagnosis of internal defects from acoustic images. Online white paper (2000). URL http://www.satech8.com. Accessed in March 2015

  89. Adams T (2002) Acoustic micro imaging finds hidden defects. Online article (2002). URL http://www.sonoscan.com. Accessed in March 2015

  90. Liu Z, Kleiner Y, Rajjani B, Wang L, Condit W (2012) Condition assessment of water transmission and distribution systems. Tech. Rep. EPA/600/R-12/017, United States Environmental Protection Agency, National Risk Management Research Laboratory, Ottawa, Ontario, Canada. (Institute for Research in Construction, National Research Council Canada)

    Google Scholar 

  91. Gan T, Hutchins D, Billson D (2002) Preliminary studies of a novel air-coupled ultrasonic inspection system for food containers. J Food Eng 53(4):315–323. doi:10.1016/S0260-8774(01)00172-8

    Article  Google Scholar 

  92. Zhu Z, Hu YC, Zhao L (2010) Gamma/x-ray linear pushbroom stereo for 3D cargo inspection. Mach Vis Appl 21(4):413–425. doi:10.1007/s00138-008-0173-8

    Article  Google Scholar 

  93. Zhu Z, Zhao L, Lei J (2005) 3D measurements in cargo inspection with a gamma-ray linear pushbroom stereo system. In: Computer Vision and Pattern Recognition—Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, pp 126–126. doi:10.1109/CVPR.2005.380

  94. Amtower R (2000) X-ray enhanced AOI. Online white paper. URL http://www.satech8.com/. Accessed in March 2015

  95. OPTO-Semiconductor HandbooK, chap. X-ray detectors, pp. 1–21. Hamamatsu. Accessed in March 2015

    Google Scholar 

  96. Pieringer C, Mery D (2010) Flaw detection in aluminum die castings using simultaneous combination of multiple views. Insight 52(10):548–552

    Article  Google Scholar 

  97. Backscatter computed tomography (2015) Website: URL http://www.inversasystems.com. Accessed in March 2015

  98. Khan M, Liu Z (2013) Backscatter computed tomography technique for the detection of damage in pipes. Technical report LTR-SMPL-2013-0071, National Research Council Canada. Ottawa, Ontario, Canada

    Google Scholar 

  99. Jansen C, Scherger B, Jordens C, Al-Naib IAI, Koch M Terahertz imaging spectroscopy for quality inspection in the food industry. Online article. URL http://www.labint-online.com. Accessed in March 2015

  100. Jördens C, Rutz F, Koch M (2006) Quality assurance of chocolate products with terahertz imaging. In: European Conference on NDT. Berlin, German, pp. 25–29

    Google Scholar 

  101. Blasch E, Liu Z, Petkie D, Ewing R, Pomrenke G, Reinhardt K (2012) Image fusion of the terahertz-visual naecon grand challenge data. In: Aerospace and Electronics Conference (NAECON), 2012 IEEE National, pp. 220–227. doi 10.1109/NAECON.2012.6531058

  102. Oka S, Mochizuki S, Togo H, Kukutsu N (2009) Inspection of concrete structures using millimeter-wave imaging technology. NTT Tech Rev 7(3):1–6

    Google Scholar 

  103. Oka S, Togo H, Kukutsu N, Nagatsuma T (2008) Latest trends in millimeter-wave imaging technology. Prog Electromagnet Res Lett 1:197–204

    Article  Google Scholar 

  104. Sheen DM, McMakin DL, Collins HD, Hall TE, Severtsen RH (1996) Concealed explosive detection on personnel using a wideband holographic millimeter-wave imaging system. In: Kadar I, Libby V (eds) Proceedings of SPIE: signal processing, sensor fusion, and target recognition, vol 2755. Orlando, FL, USA, pp. 503–513. doi:10.1117/12.243191. URL http://dx.doi.org/10.1117/12.243191

  105. adn Takafumi Kojima HT, Mochizuki S, Kukutsu N (2012) Millimeter-wave imaging for detecting surface cracks on concrete pole covered with bill-posting prevention sheet. NTT Technical Review 10(2):1–6

    Google Scholar 

  106. Mizuno M (2008) Broadband millimeter wave imaging system. J Natl Inst Inf Commun Technol 55(1):53–59

    MathSciNet  Google Scholar 

  107. He X (2015) Multispectral imaging extends vision technology capability. Photonics Spectra, USA, pp 1–4

    Google Scholar 

  108. Hart J, Resendiz E, Freid B, Sawadisavi S, Barkan C, Ahuja N (2008) Machine vision using multi-spectral imaging for undercarriage inspection of railroad equipment. In: Proceedings of the 8th world congress on railway research. Seoul, Korea

    Google Scholar 

  109. Falkenstein P (2012) Multispectral imaging plants roots in quality control. Vision Sys Des 17(11):23–25

    Google Scholar 

  110. Aleixos N, Blasco J, Navarrn F, Molt E (2002) Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput Electron Agric 33(2):121–137. doi:10.1016/S0168-1699(02)00002-9

    Article  Google Scholar 

  111. Liu Z, Forsyth DS, Komorroski JP, Hanasaki K, Kiruba K (2007) Survey: State of the art of NDE data fusion. IEEE Trans Instrum Meas 56(6):2435–2451

    Article  Google Scholar 

  112. Xiao X (1998) A multiple sensors approach to wood defect detection. Doctor dissertation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA

    Google Scholar 

  113. ibea Hamburg GmbH: Surface anomaly inspection system. Online white paper. URL http://www.ibea.de/. Accessed in March 2015

  114. Wilson A (2014) Roof tiles inspected with sound and vision. Vis Syst Des 19(3):16–17

    Google Scholar 

  115. ibea Hamburg GmbH Acoustic crack inspection for clay tiles. Online white paper. URL http://www.ibea.de/. Accessed in March 2015

  116. Luk B, Liu K, Jiang Z, Tong F (2009) Robotic impact-acoustics system for tile-wall bonding integrity inspection. Mechatronics 19(8):1251–1260. doi:http://dx.doi.org/10.1016/j.mechatronics.2009.07.006. URL http://www.sciencedirect.com/science/article/pii/S0957415809001329

  117. Tong F, Tso S, Hung M (2006) Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis. J Sound Vib 294(12):329–340. doi:10.1016/j.jsv.2005.11.017

    Article  Google Scholar 

  118. Amtower R X-ray enhanced AOI. Online white paper. URL http://www.satech8.com. Accessed in March 2015

  119. LeBlond C Combining AOI and AXI: the best of both worlds. Online white paper. URL http://www.satech8.com. Accessed in March 2015

  120. Wedowski RD, Atkinson GA, Smith ML, Smith LN (2012) A system for the dynamic industrial inspection of specular freeform surfaces. Opt Lasers Eng 50(5):632–644. doi:10.1016/j.optlaseng.2011.11.007

    Article  Google Scholar 

  121. Zhang X, Ding Y, Lv Y, Shi A, Liang R (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert Syst Appl 38(5):5930–5939. doi:10.1016/j.eswa.2010.11.030

    Article  Google Scholar 

  122. Zheng H, Kong L, Nahavandi S (2002) Automatic inspection of metallic surface defects using genetic algorithms. J Mater Process Technol 125126:427–433. doi:10.1016/S0924-0136(02)00294-7

    Article  Google Scholar 

  123. Li CJ, Zhang Z, Nakamura I, Imamura T, Miyake T, Fujiwara H (2012) Developing a new automatic vision defect inspection system for curved ssurface with hihigh specular reflection. Int J Innovative Comput Inf Control 8(7):5121–5136

    Google Scholar 

  124. Sansoni G, Trebeschi M, Docchio F (2009) State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9(1):568–601. doi:10.3390/s90100568

    Article  Google Scholar 

  125. Habel R, Laurent J, Hebert JF, Talbot M, Fox-Ivey R (2013) Use of 3D scanning technology for automated inspection of multi-modal transportation infrastructure. In: 17th IRF world meeting and exhibition. Riyadh, Saudi Arabia, pp. 1–20

    Google Scholar 

  126. Basler Making tunnels safer—basler pilot gige cameras reliably detect cracks in tunnels. Online white paper. URL http://www.baslerweb.com/. Accessed in March 2015

  127. Gavilan M, Sanchez F, Ramos JA, Marcos O (2013) Mobile inspection system for high-resolution assessment of tunnels. In: The 6th international conference on structural health monitoring of intelligent infrastructure. Hong Kong, China, pp. 1–10

    Google Scholar 

  128. Laurent J, Fox-Ivey R, Dominguez FS, Garcia JAR (2014) Use of 3D scanning technology for automated inspection of tunnels. In: Proceedings of the world tunnel congress 2014. Foz do Iguau, Brazil

    Google Scholar 

  129. Tsai Y, Li F (2012) Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3d laser technology. J Trans Eng 138(5):649–656. doi:10.1061/(ASCE)TE.1943-5436.0000353

    Article  Google Scholar 

  130. Boden F, Stasicki B (2015) Stereo camera visualize propeller. Vis Sys Des 20(2):21–25

    Google Scholar 

  131. Brosnan T, Sun DW (2004) Improving quality inspection of food products by computer visiona review. J Food Eng 61(1):3–16. doi:10.1016/S0260-8774(03)00183-3 Applications of computer vision in the food industry

    Article  Google Scholar 

  132. Cubero S, Aleixos N, Molt E, Gmez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4(4):487–504. doi:10.1007/s11947-010-0411-8

    Article  Google Scholar 

  133. Fermum L (2015) Solutions for industrial machine vision. Online article. URL http://www.vision-doctor.co.uk. Accessed in March 2015

  134. Matrox Vision library or vision-specific IDE (2012) which is right for you? matrox imaging white paper. Retrieved in March 2015

    Google Scholar 

  135. National Instruments, Austin, Texas USA (2009) NI Vision: NI Vision Builder for Automated Inspection Tutorial. Accessed in March 2015

    Google Scholar 

  136. MVTec Software GmbH, Munchen, Germany: HALCON the power of machine vision: quick guide

    Google Scholar 

  137. MVTec Software GmbH: MERLIC—a new generation of vision software. Website. URL http://www.mvtec.com/products/merlic/. Accessed in March 2015

  138. Tordivel, Oslo, Norway: scorpion vision software version X product overview. Product brochure

    Google Scholar 

  139. Teledyne Dalsa, Boston, USA: Sapera Vision Software: Sapera Essential. Accessed in March 2015

    Google Scholar 

  140. Ruiz-Altisent M, Ruiz-Garcia L, Moreda G, Lu R, Hernandez-Sanchez N, Correa E, Diezma B, Nicola B, Garc-a-Ramos J (2010) Sensors for product characterization and quality of specialty cropsa review. Comput Electron Agric 74(2):176–194. doi:10.1016/j.compag.2010.07.002

    Article  Google Scholar 

  141. Mahalik NP, Nambiar AN (2010) Trends in food packaging and manufacturing systems and technology. Trends Food Sci Technol 21(3):117–128. doi:10.1016/j.tifs.2009.12.006 Advances in Food Processing and Packaging Automation

    Article  Google Scholar 

  142. Flood N, Bailey B (2013) Vision helps dairy spot slack cheese bags. Vision Sys Des 18(9):17–22

    Google Scholar 

  143. Walker C (2014) Filters reduce glare in automotive canister inspection. Vis Syst Des 19(5):17–19

    Google Scholar 

  144. Wilson A (2015) Robotic vision system checks car fenders. Vis Syst Des 20(1):9–11

    Google Scholar 

  145. Zhou J, Lee I, Thomas B, Menassa R, Farrant A, Sansome A (2011) Applying spatial augmented reality to facilitate in-situ support for automotive spot welding inspection. In: Proceedings of the 10th international conference on virtual reality continuum and its applications in industry, VRCAI ’11. ACM, New York, NY, USA, pp 195–200. doi:10.1145/2087756.2087784

  146. Andersson A (2009) Evaluation and visualisation of surface defects on auto-body panels. J Mater Process Technol 209(2):821–837. doi:10.1016/j.jmatprotec.2008.02.078

    Article  Google Scholar 

  147. Lu Y, Tie-Qi YL, Chen J, Tisler A, Zhang J (2000) An advanced machine vision system for VFD inspection. In: PCM 2000, pp. 1–6

    Google Scholar 

  148. Yardley E (2015) Vision system inspects automotive sub-assemblies. Vis Syst Des 20(2):14–19

    Google Scholar 

  149. Duan Y, Servais P, Genest M, Ibarra-Castanedo C, Maldague X (2012) ThermoPoD: a reliability study on active infrared thermography for the inspection of composite materials. J Mech Sci Technol 26(7):1985–1991. doi:10.1007/s12206-012-0510-8

    Article  Google Scholar 

  150. Berry A, Nejikovsky B, Gibert X, Tajaddini A (2008) High speed video inspection of joint bars using advanced image collection and processing techniques. In: Proceedings of world congress on railway research. Seoul, Korea, pp. 1–13

    Google Scholar 

  151. Gibert-Serra X, Berry A, Diaz C, Jordan W, Nejikovsky B, Tajaddini A (2007) A machine vision system for automated joint bar inspection from a moving rail vehicle. In: ASME/IEEE joint rail conference and internal combustion engine spring technical conference. Pueblo, Colorado, USA

    Google Scholar 

  152. Resendiz E, Hart J, Ahuja N (2013) Automated visual inspection of railroad tracks. Intell Transp Syst IEEE Trans on 14(2):751–760. doi:10.1109/TITS.2012.2236555

    Article  Google Scholar 

  153. Resendiz E, Molina L, Hart J, Edwards R, Sawadisavi S, Ahuja N, Barkan C (2010) Development of a machine vision system for inspection of railway track components. In: 12th world conference on transport research. Lisbon, Portugal, pp. 1–22

    Google Scholar 

  154. Zhang H, Yang J, Tao W, Zhao H (2011) Vision method of inspecting missing fastening components in high-speed railway. Appl Opt 50(20):3658–3665. doi:10.1364/AO.50.003658

    Article  Google Scholar 

  155. Hart JM, Ahuja N, Barkan C, Davis DD (2004) A machine vision system for monitoring railcar health: Preliminary results. Technology Digest (TD-04-008), pp 1–4

    Google Scholar 

  156. Schlake BW, Todorovic S, Edwards JR, Hart JM, Ahuja N, Barkan CP (2010) Machine vision condition monitoring of heavy-axle load railcar structural underframe components. Proc Inst Mech Eng Part F J Rail Rapid Transit 224(5):499–511

    Article  Google Scholar 

  157. Chen TQ, Zhang J, Zhou Y, Murphey YL (2001) A smart machine vision system for PCB inspection. In: Proceedings of engineering of intelligent systems, 14th international conference on industrial and engineering applications of artificial intelligence and expert systems, IEA/AIE. Budapest, Hungary, pp. 513–518. doi 10.1007/3-540-45517-5-57

  158. Ruuska H (2009) Method for monitoring a rapidly-moving paper web and corresponding system

    Google Scholar 

  159. Qiu Z (1996) A simple machine vision system for improving the edging and trimming operations performed in hardwood sawmills. Master thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zheng Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag London (outside the USA)

About this chapter

Cite this chapter

Liu, Z., Ukida, H., Niel, K., Ramuhalli, P. (2015). Industrial Inspection with Open Eyes: Advance with Machine Vision Technology. In: Liu, Z., Ukida, H., Ramuhalli, P., Niel, K. (eds) Integrated Imaging and Vision Techniques for Industrial Inspection. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6741-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-6741-9_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6740-2

  • Online ISBN: 978-1-4471-6741-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics