Skip to main content
SpringerLink
Log in

Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The classic image processing method for flaw detection uses one image of the scene, or multiple images without correspondences between them. To improve this scheme, automated inspection using multiple views has been developed in recent years. This strategy’s key idea is to consider as real flaws those regions that can be tracked in a sequence of multiple images because they are located in positions dictated by geometric conditions. In contrast, false alarms (or noise) can be successfully eliminated in this manner, since they do not appear in the predicted places in the following images, and thus cannot be tracked. This paper presents a method to inspect aluminum wheels using images taken from different positions using a method called automatic multiple view inspection. Our method can be applied to uncalibrated image sequences, therefore, it is not necessary to determine optical and geometric parameters normally present in the calibrated systems. In addition, to improve the performance, we designed a false alarm reduction method in two and three views called intermediate classifier block (ICB). The ICB method takes advantage of the classifier ensemble methodology by making use of feature analysis in multiple views. Using this method, real flaws can be detected with high precision while most false alarms can be discriminated.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ali, M., Rueda, L., Herrera, M.: On the performance of chernoff-distance-based linear dimensionality reduction techniques. In: Proceedings of the 19th Canadian Conference on Artificial Intelligence, pp. 469–480, Quebec, Canada (2006)

  2. Boerner H., Strecker H.: Automated x-ray inspection of aluminum casting. IEEE Trans. Pattern Anal. Mach. Intell. 10(1), 79–91 (1988)

    Article  Google Scholar 

  3. Campbell J.G., Fraley C., Murtagh F., Raftery A.E.: Linear flaw detection in woven textiles using model-based clustering. Pattern Recognit. Lett. 18(14), 1539–1548 (1997)

    Article  Google Scholar 

  4. Carrasco M., Mery D.: Automated visual inspection using trifocal analysis in an uncalibrated sequence of images. Mater. Eval. 64(9), 900–906 (2006)

    Google Scholar 

  5. Carrasco, M., Mery, D.: Automatic multiple visual inspection on non-calibrated image sequence with intermediate classifier block. In: Pacific-Rim Symposium on Image and Video Technology (PSIVT’07), pp. 371–384. Springer, Berlin (2007)

  6. Carrasco, M., Pizarro, L., Mery, D.: Image acquisition and automated inspection of wine bottlenecks by tracking in multiple views. In: Proceedings of 8th International Conference on Signal Processing, Computational Geometry and Artificial Vision—ISCGAV’08, pp. 84–89. Rhodes Island, Greece (2008)

  7. Chen Z., Wu C., Shen P., Liu Y., Quan L.: A robust algorithhm to estimate the fundamental matrix. Pattern Recognit. Lett. 21, 851–861 (2000)

    Article  Google Scholar 

  8. Chin R.T., Harlow C.A.: Automated visual inspection: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 4(6), 557–573 (1982)

    Article  Google Scholar 

  9. Cohen, I., Ayache, N., Sulger, P.: Tracking points on deformable objects using curvature information. In: Proceedings of the 2nd European Conference in Computer Vision, pp. 458–466 (1992)

  10. Drury C.: Inspection Performance, pp. 2282–2314. Wiley, New York (1992)

    Google Scholar 

  11. Drury, C.G., Saran, M., Schultz, J.: Effect of fatigue/vigilance/environment on inspectors performing fluorescent penetrant and/or magnetic particle inspection. Interim Report 03-G-012, University at Buffalo, Federal Aviation Administration William J. Hughes Technical Center (2004)

  12. Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  13. Dunn O.J., Clark V.A.: Applied Statistics: Analysis of Variance and Regression. Wiley, New York (1974)

    MATH  Google Scholar 

  14. Egan J.: Signal Detection Theory and ROC Analysis. Academic Press, New York (1975)

    Google Scholar 

  15. Filbert, D., Klatte, R., Heinrich, W., Purshke, M.: Computer aided inspection of castings. In: IEEE-IAS Annual Meeting, pp. 1087–1095. Atlanta, USA (1987)

  16. Gdalyahu Y., Weinshall D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1312–1328 (1999)

    Article  Google Scholar 

  17. Haralick R.M., Shapiro L.G.: Computer and Robot Vision. Addison-Wesley, New York (1992)

    Google Scholar 

  18. Hartley R., Zisserman A.: Multiple View Geometry in Computer Vision, 1st edn. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  19. Jacob, R., Raina, S., Regunath, S., Subramanian, R., Gramopadhye, A.K.: Improving inspector’s performance and reducing errors—general aviation inspection training systems (gaits). In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting Proceedings. Aerospace Systems, Human Factors and Ergonomics Society (2004)

  20. Jarvis J.F.: Visual inspection automation. Computer 13(5), 32–38 (1980)

    Article  Google Scholar 

  21. Kita Y., Highnam R., Brady M.: Correspondence between different view breast X-rays using curved epipolar lines. Comput. Vis. Underst. 83(1), 38–56 (2001)

    Article  MATH  Google Scholar 

  22. Kumar A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Ind. Electron. 55(1), 348–363 (2008)

    Article  Google Scholar 

  23. Lagarias J., Reeds J.A., Wright M.H., Wright P.E.: Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  24. Liu H., Srinath M.: Partial shape classification using contour matching in distance transformation. IEEE Trans. Pattern Anal. Mach. Intell. 12(11), 1072–1079 (1990)

    Article  Google Scholar 

  25. Malamas E., Petrakis E.G., Zervakis M.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21(2), 171–188 (2003)

    Article  Google Scholar 

  26. Mery D.: Crossing line profile: a new approach to detecting defects in aluminium castings. Lect. Notes Comput. Sci. 2749, 725–732 (2003)

    Article  Google Scholar 

  27. Mery D.: High contrast pixels: a new feature for defect detection in x-ray testing. Insight 46(12), 751–753 (2006)

    Article  Google Scholar 

  28. Mery D., Carrasco M.: Automated multiple view inspection based on uncalibrated image sequence. Lect. Notes Comput. Sci. 3540, 1238–1247 (2005)

    Article  Google Scholar 

  29. Mery D., Filbert D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robot. Autom. 18(6), 890–901 (2002)

    Article  Google Scholar 

  30. Mery, D., Filbert, D.: Classification of potential defects in automated inspection of aluminium castings using statistical pattern recognition. In: Proceedings of 8th European Conference on Non-Destructive Testing (ECNDT 2002), Barcelona, Spain (2002)

  31. Mery D., da Silva R.R., Calôba L.P., Rebello J.M.A.: Patter recognition in the automatic inspection of aluminium castings. Insight 45(7), 475–483 (2003)

    Article  Google Scholar 

  32. Mital, A., Govindaraju, M., Subramani, B.: A comparison between manual and hybrid methods in parts inspections, vol. 9, issue 6. Integrated Manufacturing Systems, MCB UP Ltd, Bradford (1998)

  33. Newman T.S., Jain A.K.: A survey of automated visual inspection. Comput. Vis. Image Underst. 61(2), 231–262 (1995)

    Article  Google Scholar 

  34. Nguyen, V.D., Noble, A., Mundy, J., Janning, J., Ross, J.: Exhaustive detection of manufacturing flaws as abnormalities. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 945–952 (1998)

  35. Pedreschi F., Mery D., Mendoza F., Aguilera J.: Classification of potato chips using pattern recognition. J. Food Sci. 69(6), E264–E270 (2004)

    Article  Google Scholar 

  36. Pizarro L., Mery D., Delpiano R., Carrasco M.: Robust automated multiple view inspection. Pattern Anal. Appl. 11(1), 21–32 (2008)

    Article  MathSciNet  Google Scholar 

  37. Polikar R.: Ensemble systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  38. Sebastian T., Klein P., Kimia B.: On aligning curves. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 116–125 (2003)

    Article  Google Scholar 

  39. Sonka, M., Hlavac, V., Boyle, R. (eds): Image Processing, Analysis and Machine Vision, 2nd edn. PWS Publishing, Pacific Grove (1999)

    Google Scholar 

  40. Spencer, F.: Visual inspection research project report on benchmark inspections. Technical Report DOT/FAA/AR-96/65, U.S. Department of Transportation, Federal Aviation Administration, Washington, DC, Office of Aviation Research Washington, D.C. 20591 (1996)

  41. Spicer, P., Bohl, K., Abramovich, G., Barhak, J.: Robust calibration of a reconfigurable camera array for machine vision inspection (RAMVI): using rule-based colour recognition. In: Proceedings of the 1st International Conference on Computer Vision Theory and Applications Ultrasonics Symposium (VISAPP), pp. 131–138, Setúbal, Portugal (2006)

  42. Stearns, S.: On selecting features for patterns classifiers. In: Proceedings of IAPR International Conference on Pattern Recognition, pp. 71–75 (1976)

  43. Umeyama S.: Parameterized point pattern matching and its application to recognition of object families. IEEE Trans. Pattern Anal. Mach. Intell. 15(2), 136–144 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escuela de Ingeniería Informática, Facultad de Ingeniería, Universidad Diego Portales, Av. Ejército 441, Santiago, Chile

    Miguel Carrasco

  2. Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860 (143), Santiago, Chile

    Miguel Carrasco & Domingo Mery

Authors
  1. Miguel Carrasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Domingo Mery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel Carrasco.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carrasco, M., Mery, D. Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels. Machine Vision and Applications 22, 157–170 (2011). https://doi.org/10.1007/s00138-010-0255-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-010-0255-2

Keywords

Search

Navigation