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RETRACTED ARTICLE: The resin lens flaw feature extraction and detection system based on data transmission security

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This article was retracted on 13 September 2022

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Abstract

Data security and privacy have become a problem which people pay more attention to. In the process of feature extraction and detection of lens defects, the security of data transmission becomes more and more important. This paper according to the characteristics of the lens defect extraction method is studied and discussed through a single scan of the two boundary value of graphics and images, without the need to fill the region, does not need the help of chain codes statistical region number and boundary information. According to the established balanced binary search tree, the area and perimeter of each defect were calculated. This method works fast, and it needs only small amount of calculation; it can suppress noise better and accurate. This paper combines the detection of networking and information exchange, in order to ensure the normal lens defects in feature extraction efficiency. And comparing several encryption algorithms in the data transmission process, selecting the best storage and encryption technology to ensure data security and improve the security of the system.

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References

  1. Abdelwahab MM (2015) High performance FPGA implementation of Data encryption standard. In: International conference on computing, control, networking, electronics and embedded systems engineering, IEEE, pp 37–40

  2. Bovik AC (2016) Introduction to digital image processing[J]. The Essential Guide to Image Processing 48(Special_Issue):S106–S107

  3. Chang V (2017a) Data analytics and visualization for inspecting cancers and genes. Multimed Tools Appl 3:1–15

  4. Chang V (2017b) A proposed social network analysis platform for big data analytics. Technol Forecast Soc Chang 130:57–68

  5. Chang V, Ramachandran M (2016) Towards achieving data security with the cloud computing adoption framework. IEEE Trans Serv Comput 9(1):138–151

  6. Chen Y, Shang L (2016). Improved sift image registration algorithm on characteristic statistical distributions and consistency constraint. Optik - International Journal for Light and Electron Optics 127(2):900–911

  7. Conti M, Dehghantanha A, Franke K, Watson S (2017) Internet of things security and forensics: challenges and opportunities. Futur Gener Comput Syst 78:544–546

  8. Costarelli D, Vinti G (2015) Multivariate sampling Kantorovich operators: from the theory to the Digital Image Processing algorithm, vol 15. In: Pamm - Proceedings in applied mathematics and mechanics, special issue: meeting of the international association of applied mathematics and mechanics, Lecce, 23-27 March, pp 655–656

  9. Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K (2017) Towards fog-driven iot ehealth: promises and challenges of iot in medicine and healthcare. Futur Gener Comput Syst 78:659–676

  10. Ghazisaidi N, Maier M, Reisslein M (2012) Vmp: a mac protocol for epon-based video-dominated fiwi access networks. IEEE Trans Broadcast 58(3):440–453

  11. He X, Machanavajjhala A, Ding B (2014) Blowfish privacy: tuning privacy-utility trade-offs using policies. In: ACM SIGMOD international conference on management of data, ACM, pp 1447–1458

  12. Keys RG (2003) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160

  13. Kmieć M, Glowacz A (2015) Object detection in security applications using dominant edge directions. Elsevier Science Inc., New York

  14. Kumar S, Gupta P (2014) A comparative analysis of sha and md5 algorithm. International Journal of Computer Science & Information Technolo 5(3):4492–4495

  15. Kumara SV, Benakop P (2017) Predominance of blowfish over triple data encryption standard symmetric key algorithm for secure integrated circuits using verilog hdl. Int J Net Sec Appl 9(6):29–38

  16. Lei L, Chen T, Li ZY, Su Y (2008) New approach to image invariant extraction under global affine transformation. Journal of National University of Defense Technology 30(4):64–70

    Google Scholar 

  17. Liao D, Sun G, Li H, Yu H, Chang V (2017) The framework and algorithm for preserving user trajectory while using location-based services in iot-cloud systems. Clust Comput 20(2):1–15

  18. Liu LL (1998) New algorithm and its application for edge detection in morphology. J Infrared Millim Waves 17(5):386–390

  19. Lu Y, Zhang Q, Wei B (2015) Real-time CPU based H.265/HEVC encoding solution with x86 platform technology. In: International conference on computing, networking and communications, IEEE, pp 418–421

  20. Moustafa AA, Alqadi ZA (2009) A practical approach of selecting the edge detector parameters to achieve a good edge map of the gray image. J Comput Sci 5(5):355–362

  21. Ora P, Pal PR (2015) Data security and integrity in cloud computing based on RSA partial homomorphic and MD5 cryptography. International Conference on Computer, Communication and Control, IEEE, pp 1–6

  22. Poonia V, Yadav NS (2015) Analysis of modified Blowfish algorithm in different cases with various parameters. In: International conference on advanced computing and communication systems, IEEE, pp 1–5

  23. Poulios K, Renard Y (2015) An unconstrained integral approximation of large sliding frictional contact between deformable solids. Comput Struct 153:75–90

  24. Sohal AS, Sandhu R, Sood SK, Chang V (2017) A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments. Comput Secur 74:340–354

  25. Sumengen B, Manjunath BS (2015) Multi-scale edge detection and image segmentation. In: Signal Processing Conference, 2005. IEEE, European, pp 1–4

  26. Sun G, Chang V, Ramachandran M, Sun Z, Li G, Yu H, et al. (2016) Efficient location privacy algorithm for internet of things (iot) services and applications. J Netw Comput Appl 89(C):3–13

  27. Tardieu F, Granier C (2014) A critical reassessment of evolutionary algorithms on the cryptanalysis of the simplified data encryption standard algorithm. Eprint Arxiv 4(2):555–567

  28. Wang W, Zhang D, Zhang Y, Beijing (2010) Fast spatial verification with affine transformations. Journal of Computer-Aided Design & Computer Graphics 22(4):625–631

  29. Wang Q, Li G, Wang C, Sun W (2015) Dual probability selection mapping algorithm for video transmission over wireless sensor networks. J Commun 10:192–198

  30. Wu Z, Lei L, Dong J, Hou J, Zhang X (2014) Reconfigurable temporal fourier transformation and temporal imaging. J Lightwave Technol 32(23):4565–4570

  31. Xie J-L, Li L-S, Lin G-X (2014) The method of calculating the area and circumference based on the boundary trace [J]. Electron Tech Softw Eng 9:119–120

  32. Yang Y, Zheng X, Chang V, Ye S, Tang C (2017) Lattice assumption based fuzzy information retrieval scheme support multi-user for secure multimedia cloud. Multimed Tools Appl 1:1–15

  33. Yang Y, Zheng X, Guo W, Liu X, Chang V (2018) Privacy-preserving fusion of iot and big data for e-health. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.01.003

  34. Yeh JP (2012) Detecting edge using support vector machine. Adv Mater Res 588-589:974–977

  35. Zhang B, Chen ZC, Zheng LF, Tong QX, Liu YN, Yang YD, et al. (2004) Object detection based on feature extraction from hyperspectral imagery and convex cone projection transform. J Infrared Millim Waves 23(6):441–445+450

  36. Zhong L, Wan W, Kong D (2017) Javaweb login authentication based on improved MD5 algorithm. In: International conference on audio, language and image processing, IEEE, pp 131–135

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Correspondence to Hongmin Wang.

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Wang, H., Zhu, H., Xue, P. et al. RETRACTED ARTICLE: The resin lens flaw feature extraction and detection system based on data transmission security. Multimed Tools Appl 77, 18483–18501 (2018). https://doi.org/10.1007/s11042-018-5774-2

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  • DOI: https://doi.org/10.1007/s11042-018-5774-2

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