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A mobilized automatic human body measure system using neural network

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Abstract

Mobilized automatic human body measurement systems possess high mobility, easy operation, and reasonable accuracy. However, existing systems focus on accuracy and robustness rather than mobility and convenience. To overcome this shortcoming, this work presents a mobilized automatic human body measure system using a neural network (MaHuMS-NN) to promote general measurement results by supervised learning. MaHuMS-NN based on general regression NN (GRNN) selects an image, performs image processing, segments the image, and detects a silhouette for feature point extraction in the silhouette. The system measures feature size. The significant contributions of this work are as follows. First, MaHuMS-NN is the first intelligent system for anthropometry in the Android platform. Second, unlike existing systems, MaHuMS-NN can intelligently adjust when the model is optimized for prediction and perform self-error correction based on individual characteristics. Experimental results indicate that compared with existing systems, MaHuMS-NN demonstrates better performance with an accuracy of less than 0.03 m.

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References

  1. Adikari A, Ganegoda N, Wanniarachchi W (2017) Non-contact human body parameter measurement based on Kinect sensor, IOSRJ. Comput Eng 19:80–85

    Google Scholar 

  2. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Comput Vis 2001 ICCV 2001 Proc Eighth IEEE Int Conf IEEE: 105–112

  3. Chen G-Q, Kong H-Y, Tan F, Huang L, Liu G-L (2011) Research of non-contact 2D body measurement system, in: Control Autom Syst Eng CASE 2011 Int Conf IEEE: 1–4

  4. Cheng M-M, Mitra NJ, Huang X, Torr PH, Hu S-M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37:569–582

    Article  Google Scholar 

  5. Choi W | Tailor Measure App Available on the App Store Now. http://wingchoi.com/ibody-measures/ (accessed June 20, 2017)

  6. D’Apuzzo N, Gruen A (2009) Recent advances in 3D full body scanning with applications to fashion and apparel, opt. 3- Meas. Tech IX

  7. Daanen HA, Taylor SE, Brunsman MA, Nurre JH Absolute accuracy of the Cyberware WB4 whole-body scanner. Electron. Imaging 97 Int Soc Opt Photo 1997:6–12

  8. Dao N-L, Deng T, Cai J (2014) Fast and automatic body circular measurement based on a single Kinect. Asia-Pac Sign Inf Proces Assoc 2014 Annu Summit Conf APSIPA IEEE: 1–4

  9. Davalo E, Naïm P (1991) Applications of neural networks. Macmillan Education, UK

    Google Scholar 

  10. DITUS. http://www.leatech.net/plus/view.php?aid=338 (accessed June 20, 2017)

  11. Geng L, Xiao Z, Wu J, Zhang F, Miao J (2013) Monocular vision distance measurement method based on dynamic error compensation. Int J Digit Content Technol Appl 7:230–239

    Article  Google Scholar 

  12. Hurley JD, Demers MH, Wulpern RC, Grindon JR (1997) Body measurement system using white light projected patterns for made-to-measure apparel. Opt Sci Eng Instrumentation97 Int Soc Optics Photo: 212–223

  13. Indiegogo: From concept to market with crowdfunding. https://www.indiegogo.com/#/picks_for_you (accessed June 20, 2017)

  14. IWODE. http://www.iwode.com/comments-media-reports/item/14-iwode-%20app.html (accessed June 20, 2017)

  15. Jones PR, West GM, Harris DH, Read JB (1989) The Loughborough anthropometric shadow scanner (LASS). Endeavour 13:162–168

    Article  Google Scholar 

  16. Kaufmann K (1997) Invasion of the body scanners. IEEE Circ Dev Mag 13:12–17

    Article  Google Scholar 

  17. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14:1137–1145

    Google Scholar 

  18. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  19. Li J (2017) Binocular vision measurement method for relative position and attitude based on dual-quaternion, J Mod Opt: 1–8

  20. L-Y X, Z-Q Cao P, Zhao C, Zhou A (2017) New monocular vision measurement method to estimate 3D positions of objects on floor. Int J Autom Comput 14:1–10

    Article  Google Scholar 

  21. Meadows DM, Johnson WO, Allen JB (1970) Generation of surface contours by moiré patterns. Appl Opt 9:942–947

    Article  Google Scholar 

  22. Pargas R (1998) Automating information extraction from 3D scan data. DTIC Document

  23. Peng S, Sun X, Liu G, Yang A (2005) Survey on 3D human body auto measurement technology. Appl Res Comput

  24. Perez JM, Schreiner S, Gorton GE (2006) Evaluation of the VITUS smart laser scanner for accuracy, resolution and repeatability for clinical assessment of pectus deformities and scoliosis. Bioeng Conf 2006 Proc IEEE 32nd Annu Northeast IEEE: 33–34

  25. Peyer KE, Morris M, Sellers WI (2015) Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras. Peer J 3:e831

    Article  Google Scholar 

  26. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph TOG ACM: 309–314

    Article  Google Scholar 

  27. Schnitzer JK, Rice DJ, Robert Iii CF, Zajkowski AJ (2003) Data Normalization, US

  28. Simmons KP (2001) Body measurement techniques: a comparison of three-dimensional body scanning and physical anthropometric methods, North Carolina State University

  29. Tang M, Gorelick L, Veksler O, Boykov Y (2013) Grabcut in one cut. Proc IEEE Int Conf Comput Vis: 1769–1776

  30. Tong J, Zhou J, Liu L, Pan Z, Yan H (2012) Scanning 3d full human bodies using kinects. IEEE Trans Vis Comput Graph 18:643–650

    Article  Google Scholar 

  31. Treleaven P, Wells J (2007) 3D body scanning and healthcare applications. Computer 40

    Article  Google Scholar 

  32. Uhm T, Park H, Park J-I (2015) Fully vision-based automatic human body measurement system for apparel application. Measurement 61:169–179

    Article  Google Scholar 

  33. VisImage Systems-Technical Information-BoSS-21 Principle. http://www.vis.ca/product.asp?id=17 (accessed June 20, 2017)

  34. Weiss A, Hirshberg D, Black MJ (2011) Home 3D body scans from noisy image and range data. Comput Vis ICCV 2011 IEEE Int Conf IEEE: 1951–1958

  35. Xie Q, Xu J, Nie YY et al. (2008) GB/T 16160–2008 location and method of anthropometric surveys for garments

  36. Xu HM, Xia LK, Yang Q, Liu YJ, Tang SY, Yang J (2011) Human featured automatic measurement system based on vanishing points (HuFAMS-VP). Contrl Autom Syst Eng CASE 2011 Int Conf IEEE: 1–4

  37. Xu H, Yu Y, Zhou Y, Li Y, Du S (2013) Measuring accurate body parameters of dressed humans with large-scale motion using a Kinect sensor. Sensors 13:11362–11384

    Article  Google Scholar 

  38. Yamauchi K, Sato Y (2006) 3D human body measurement by multiple range images. Pattern Recogn 2006 ICPR 2006 18th Int Conf IEEE: 833–836

  39. Yan CG, Xie HT, Liu S, Yin J, Zhang YD, Dai QH (2018) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 19(1):220–229

    Article  Google Scholar 

  40. Yan CG, Xie HT, Yang DB, Yin J, Zhang YD, Dai QH (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295

    Article  Google Scholar 

  41. Yu W, Harlock S, Yeung K (1995) Contour measurements of moulded brassiere cups using a shadow moire technique. Proc Third Asian Text Conf Hong Kong: 300–8

  42. Yuan T. http://xuguojun.51sole.com/ (accessed June 20, 2017)

  43. Zhang Q, Gupta KC (2000) Neural Network for RF and microwave design (Book+ Neuromodeler Disk), Artech House, Inc

  44. Zhang X, Zhu L, Chu L (2011) Evaluation of coded structured light methods using ground truth. Cybern Intell Syst CIS 2011 IEEE 5th Int Conf IEEE: 117–123

  45. Zhao Y, Rada L, Chen K, Harding SP, Zheng Y (2015) Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans Med Imaging 34:1797–1807

    Article  Google Scholar 

  46. Zhou XJ (2008) Reconstructing 3D virtual humans from photo-realistic images. J Southeast Univ Nat Sci Ed 4:018

    Google Scholar 

  47. Zhou P, Wang CJ, Chen X (2005) Monocular computer vision measurement system. Opto-Electron Eng 32:90–93

    Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61572076), Beijing Advanced Innovation Center for Imaging Technology.

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Correspondence to Jian Yang.

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Xia, L., Yang, J., Han, T. et al. A mobilized automatic human body measure system using neural network. Multimed Tools Appl 78, 11291–11311 (2019). https://doi.org/10.1007/s11042-018-6645-6

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  • DOI: https://doi.org/10.1007/s11042-018-6645-6

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