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Analysis of a printed complex image quality checking method of fabric cloth for development of an automated quality checking system

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Abstract

In this research paper, the method of printed complex image quality checking for fabric cloth has been proposed for the development of an automated, printed image, quality checking system. As described in this paper, this quality checking system was implemented using graph matching and the pixel calculation algorithms, and the reliability was analyzed using HALCON software. The analysis of image and defect detection of image was performed by partitioning the images. First, the image registration area was automatically selected according to the regional characteristics of the image pattern; subsequently, the system performed fast image registration, based on the shape discovered through a graph matching method. Finally, the pixel calculation method was used in the specific image registration area to detect defects of the printed image in order to identify and ensure quality of the subsequently printed complex pattern on the fabric cloth. In this research, the proposed method was carried out on the image pattern of a selected registration area. The image registration for defective detection quickly, effectively, and efficiently detected the faults. The minimum and maximum errors were found as 0.16% and 8.4%, respectively, occurring within a 10% control. Whenever the error exceeded 10%, it was regarded as a quality defect of printed fabric cloth product; conversely, when the error fell below 10%, the product was deemed a qualified product. The higher measurement accuracy by the proposed method meets the needs of actual production by providing a new method of printing defect detection for the textile and garment industry printing sectors.

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References

  1. Houqing, L.: In the decorative features of the theory of design in the clothing design. Suzhou university, Suzhou (2008)

    Google Scholar 

  2. Gong, L., Shin, J.: The innovative application of surface texture in fashion and textile design. Fash. Text. Res. J. 15(3), 336–346 (2013)

    Article  Google Scholar 

  3. Ting, F.: Impact of labor cost changes on the competitiveness of China’s textile industry. Intl. J. Econ. Commer. Manag. III(2), 1–8 (2015)

    Google Scholar 

  4. Feng, L., Yan Gangling, Y.: Analysis of the improvement of clothing quality and brand. Sci. Technol. Innov. Herald 27, 200 (2014)

    Google Scholar 

  5. Agac, S., Sakarya, L.M.: Evaluation of outdoor sports clothing brand personality by users. Intl. J. Sci. Cult. Sport (2014). https://doi.org/10.14486/IJSCS

    Article  Google Scholar 

  6. Zhou, Y.S., Zhang, G., Jia, L.: Application of digital image processing technology in interferometer. Intl. J. Online Biomed. Eng. 9(6), 65–68 (2013)

    Google Scholar 

  7. Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)

    Article  MathSciNet  Google Scholar 

  8. Huaiyuan, S., Yuehua, L., Fuzhi, Z., Yijun, H.: Batch number detection technology of drug packaging bottle based on Halcon. Packag. Eng. 08, 71–73 (2008)

    Google Scholar 

  9. Amoako-Yirenkyi, P., Appati, J.K., Dontwi, I.K.: Performance analysis of image smoothing techniques on a new fractional convolution mask for image edge detection. Open J. Appl. Sci. 6, 478–488 (2016)

    MATH  Google Scholar 

  10. Norberg, O., Westin, P., Lindberg, S., Klaman, M., Eidenvall, L.: A comparison of print quality between digital, offset and flexographic printing presses performed on different paper qualities. In: International Conference on Digital Production Printing and Industrial Applications, IS&T, pp. 380–385 (2001)

  11. Lindberg, S.: Perceptual Determinants of Print Quality. PhD thesis, Stockholm University (2004)

  12. Gast, G., Tse, M.K.: A report on a subjective print quality survey conducted at NIP16. In: NIP17: International Conference on Digital Printing Technologies, IS&T, pp. 723–727(2001)

  13. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)

  14. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3166–3173 (2013)

  15. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing Markov chain. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1665–1672 (2013)

  16. Han, J., Zhang, D., Hu, X., Guo, L., Ren, J., Wu, F.: Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits Syst. Video Technol. 25(8), 1309–1321 (2015)

    Article  Google Scholar 

  17. Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 110–119 (2015)

  18. Von Neumann, J.: The general and logical theory of automata. Cereb. Mech. Behav. 1(41), 1–2 (1951)

    MathSciNet  Google Scholar 

  19. Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 FPS. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1404–1412 (2015)

  20. Tu, W.C., He, S., Yang, Q., Chien, S.Y.: Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2334–2342 (2016)

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    Authors and Affiliations

    1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

      S. M. Jahangir Alam & Guoqing Hu

    2. Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna, Bangladesh

      Saumendu Roy

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    1. S. M. Jahangir Alam
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    2. Guoqing Hu
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    3. Saumendu Roy
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    Contributions

    Dr. SMJA is the main contributor to develop this research work and checked the validity of the data for reconfirming. Professor Dr. GH is a coauthor who was revised the contents and text. Mr. SR checked the literatures and structure of the text.

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    Correspondence to S. M. Jahangir Alam.

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    Jahangir Alam, S.M., Hu, G. & Roy, S. Analysis of a printed complex image quality checking method of fabric cloth for development of an automated quality checking system. SIViP 15, 195–203 (2021). https://doi.org/10.1007/s11760-020-01737-w

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    • DOI: https://doi.org/10.1007/s11760-020-01737-w

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