Quality Assessment of 3D Prints Based on Feature Similarity Metrics

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 525)

Abstract

Visual quality inspection of 3D prints is one of the most recent challenges in image quality assessment domain. One of the natural approaches to this issue seems to be the use of some existing metrics successfully applied to general image quality assessment purposes. Since the application of basic Structural Similarity does not lead to satisfactory quality prediction of 3D prints, in this paper some experimental results obtained using feature based metrics have been presented. Due to the use of different colors of filaments the influence of color to grayscale conversion method has also been analyzed. Proposed approach leads to promising results allowing a reliable prediction of 3D prints quality for different colors of filaments.

References

  1. 1.
    Chauhan, V., Surgenor, B.: A comparative study of machine vision based methods for fault detection in an automated assembly machine. Procedia Manufact. 1, 416–428 (2015)CrossRefGoogle Scholar
  2. 2.
    Cheng, Y., Jafari, M.A.: Vision-based online process control in manufacturing applications. IEEE Trans. Autom. Sci. Eng. 5(1), 140–153 (2008)CrossRefGoogle Scholar
  3. 3.
    Fang, T., Jafari, M.A., Bakhadyrov, I., Safari, A., Danforth, S., Langrana, N.: Online defect detection in layered manufacturing using process signature. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, San Diego, California, USA, vol. 5, pp. 4373–4378, October 1998Google Scholar
  4. 4.
    International Telecommunication Union: Recommendation BT.709-5 - Parameter values for the HDTV standards for production and international programme exchange (2002)Google Scholar
  5. 5.
    International Telecommunication Union: Recommendation BT.601-7 - Studio encoding parameters of digital television for standard 4:3 and wide-screen 16:9 aspect ratios (2011)Google Scholar
  6. 6.
    Liu, Z., Laganière, R.: Phase congruence measurement for image similarity assessment. Pattern Recogn. Lett. 28(1), 166–172 (2007)CrossRefGoogle Scholar
  7. 7.
    Straub, J.: Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2), 55–71 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Szkilnyk, G., Hughes, K., Surgenor, B.: Vision based fault detection of automated assembly equipment. In: Proceedings of ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B, Washington, DC, USA, vol. 3, pp. 691–697, August 2011Google Scholar
  9. 9.
    Tourloukis, G., Stoyanov, S., Tilford, T., Bailey, C.: Data driven approach to quality assessment of 3D printed electronic products. In: Proceedings of 38th International Spring Seminar on Electronics Technology (ISSE), Eger, Hungary, pp. 300–305, May 2015Google Scholar
  10. 10.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Sig. Process. Lett. 9(3), 81–84 (2002)CrossRefGoogle Scholar
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H., Simoncelli, E.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Simoncelli, E., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California (2003)Google Scholar
  13. 13.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, L., Zhang, L., Mou, X.: RFSIM: A feature based image quality assessment metric using Riesz transforms. In: Proceedings of the 17th IEEE International Conference on Image Processing, Hong Kong, China, pp. 321–324 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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