Abstract
The paper is focused on the issue of visual quality assessment of 3D printed surfaces which can be helpful in detection of quality decrease during the printing process as well as the quality inspection of previously printed objects. The basic assumption of the proposed approach is the fact that each distortion of the regular patterns, visible on the side surfaces of objects printed using Fused Deposition Modeling (FDM) technology, causes the increase of the local image entropy. However, due to different colors of the filaments used in our experiments, a reliable prediction of the absolute entropy values can be troublesome. The proposed solution utilizes the combined quality indicator based on the entropy and its variance calculated for the hue component, as well as for the RGB channels, depending on the color of the filament, allowing proper detection of low quality surfaces regardless of the filament’s color.
Keywords
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Battisti, F., Bosc, E., Carli, M., Callet, P.L., Perugia, S.: Objective image quality assessment of 3D synthesized views. Signal Process. Image Commun. 30, 78–88 (2015)
Benoit, A., Le Callet, P., Campisi, P., Cousseau, R.: Quality assessment of stereoscopic images. EURASIP J. Image Video Process. 2008(1), 659024 (2008)
Chandler, D.M.: Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Process. 2013, 53 (2013)
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)
Chen, M.J., Cormack, L.K., Bovik, A.C.: No-reference quality assessment of natural stereopairs. IEEE Trans. Image Process. 22(9), 3379–3391 (2013)
Chen, M.J., Su, C.C., Kwon, D.K., Cormack, L.K., Bovik, A.C.: Full-reference quality assessment of stereopairs accounting for rivalry. Signal Process. Image Commun. 28(9), 1143–1155 (2013)
Cheng, Y., Jafari, M.A.: Vision-based online process control in manufacturing applications. IEEE Trans. Autom. Sci. Eng. 5(1), 140–153 (2008)
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 the IEEE International Conference on Systems, Man and Cybernetics, vol. 5, San Diego, California, USA, pp. 4373–4378 (1998)
Fastowicz, J., Okarma, K.: Texture based quality assessment of 3D prints for different lighting conditions. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 17–28. Springer, Cham (2016). doi:10.1007/978-3-319-46418-3_2
Goldmann, L., Simone, F.D., Ebrahimi, T.: A comprehensive database and subjective evaluation methodology for quality of experience in stereoscopic video. In: 3D Image Processing (3DIP) and Applications. Proceedings of SPIE, No. 7526 (2010)
Guo, J., Vidal, V., Cheng, I., Basu, A., Baskurt, A., Lavoue, G.: Subjective and objective visual quality assessment of textured 3D meshes. ACM Trans. Appl. Percept. 14(2), 11:1–11:20 (2016)
Lin, Y., Wu, J.: Quality assessment of stereoscopic 3D image compression by binocular integration behaviors. IEEE Trans. Image Process. 23(4), 1527–1542 (2014)
Moughlbay, A.A., Cervera, E., Martinet, P.: Model based visual servoing tasks with an autonomous humanoid robot. In: Lee, S. (ed.) Frontiers of Intelligent Autonomous Systems. SCI, vol. 466, pp. 149–162. Springer, Heidelberg (2013). doi:10.1007/978-3-642-35485-4_12
Okarma, K., Fastowicz, J.: No-reference quality assessment of 3D prints based on the GLCM analysis. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 788–793 (2016)
Okarma, K., Fastowicz, J.: Quality assessment of 3D prints based on feature similarity metrics. In: Choraś, R. (ed.) Image Processing and Communications Challenges 8. AISC, vol. 525, pp. 104–111. Springer, Cham (2016). doi:10.1007/978-3-319-47274-4_12
Okarma, K., Fastowicz, J., Tecław, M.: Application of structural similarity based metrics for quality assessment of 3D prints. In: Chmielewski, L.J., Datta, A., Kozera, R., Wojciechowski, K. (eds.) ICCVG 2016. LNCS, vol. 9972, pp. 244–252. Springer, Cham (2016). doi:10.1007/978-3-319-46418-3_22
Starch, J., Kilner, J., Hilton, A.: Objective quality assessment in free-viewpoint video production. In: Proceedings of the 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, Istanbul, Turkey, pp. 225–228 (2008)
Straub, J.: Initial work on the characterization of additive manufacturing (3D printing) using software image analysis. Machines 3(2), 55–71 (2015)
Szkilnyk, G., Hughes, K., Surgenor, B.: Vision based fault detection of automated assembly equipment. In: Proceedings of the ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B, vol. 3, Washington, DC, USA, pp. 691–697 (2011)
Tourloukis, G., Stoyanov, S., Tilford, T., Bailey, C.: Data driven approach to quality assessment of 3D printed electronic products. In: Proceedings of the 38th International Spring Seminar on Electronics Technology (ISSE), Eger, Hungary, pp. 300–305, May 2015
Yang, J., Hou, C., Zhou, Y., Zhang, Z., Guo, J.: Objective quality assessment method of stereo images. In: Proceedings of the 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, Potsdam, Germany, pp. 1–4 (2009)
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Okarma, K., Fastowicz, J. (2018). Color Independent Quality Assessment of 3D Printed Surfaces Based on Image Entropy. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_32
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DOI: https://doi.org/10.1007/978-3-319-59162-9_32
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