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Signal, Image and Video Processing

, Volume 10, Issue 4, pp 753–760 | Cite as

Real-time, automatic shape-changing robot adjustment and gender classification

  • Morteza Daneshmand
  • Alvo Aabloo
  • Cagri Ozcinar
  • Gholamreza Anbarjafari
Original Paper

Abstract

This paper introduces the results of novel theoretical and practical studies aimed at providing automatic and accurate real-time activation and adjustment of shape-changing robots in accord to the shape of the body of the user. The proposed method consists of scanning, classifying the instances according to gender and size, performing analysis on both the user’s body and the prospective garment, which is be virtually fitted, modelling, extracting measurements and assigning reference points on them, segmenting the 3D visual data imported from the shape-changing robot, and finally, superimposing, adopting and depicting the resulting garment model on the user’s body. The estimation process of the positions of the moving actuators for adjusting the shape-changing robots tries to determine which input values could result in the closest representation of the desired sizes and distances through devising the mathematical description of a map relating them to each other. In order to classify the data obtained by the 3D scanner, first maximum likelihood function is used for selecting one of the shape-changing robots, according to the presumed gender and size, to be activated, and subsequently, support vector machine is utilized so as to find out which shape template from the dictionary best matches the scanning instance being considered. As a use case, the proposed method is applied to the visual data obtained by scanning Fits.me’s shape-changing robots using 3D laser scanner. The methods currently used are manual, whereas the proposed method is automatic and the experimental results show that it is the accurate and reliable.

Keywords

Gender classification Supervised learning Size-dictionary Shape-changing robots 3D scanning 

References

  1. 1.
    Tong, J., Zhou, J., Liu, L., Pan, Z., Yan, H.: Scanning 3D full human bodies using kinects. Trans. Visual. Comput. Graph. 18(4), 643–650 (2012)CrossRefGoogle Scholar
  2. 2.
    Zhou, Z., Shu, B., Zhuo, S., Deng, X., Tan, P., Lin, S.: Image-based clothes animation for virtual fitting. In: 5th SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia, Technical Briefs, p. 33. ACM (2012)Google Scholar
  3. 3.
    Zhang, Y., Sun, Z., Liu, K., Zhang, Y.: A method of 3D garment model generation using sketchy contours. In: Sixth International Conference on Computer Graphics, Imaging and Visualization, pp. 205–210. IEEE (2009)Google Scholar
  4. 4.
    Sengupta, S., Chaudhuri, P.: Virtual garment simulation. In: Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)Google Scholar
  5. 5.
    Fezza, S.A., Larabi, M.-C.: Color calibration of multi-view video plus depth for advanced 3D video. Signal Image Video Process. 1–15 (2015). doi: 10.1007/s11760-015-0761-9
  6. 6.
    Ma, Y., Zheng, J., Xie, J.: Foldover-free mesh warping for constrained texture mapping. IEEE Trans. Visual. Comput. Graph. 21(3), 375–388 (2015)CrossRefGoogle Scholar
  7. 7.
    Liu, L., Zhang, L., Xu, Y., Gotsman, C., Gortler, S.J.: A local/global approach to mesh parameterization. In: Eurographics Symposium on Geometry Processing, vol. 27, no. 5, pp. 1495–1504. Blackwell Publishing (2008)Google Scholar
  8. 8.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments. In: 12th International Symposium on Experimental Robotics (ISER), Citeseer (2010)Google Scholar
  9. 9.
    Pop, C.I., Ionescu, C., De Keyser, R., Dulf, E.H.: Robustness evaluation of fractional order control for varying time delay processes. Signal Image Video Process. 6(3), 453–461 (2012)CrossRefGoogle Scholar
  10. 10.
    Al-Jumaily, A., Olivares, R.A.: Bio-driven system-based virtual reality for prosthetic and rehabilitation systems. Signal Image Video Process. 6(1), 71–84 (2012)CrossRefGoogle Scholar
  11. 11.
    Junejo, I.N., Bhutta, A.A., Foroosh, H.: Single-class SVM for dynamic scene modeling. Signal Image Video Process. 7(1), 45–52 (2013)CrossRefGoogle Scholar
  12. 12.
    Pereira, F., Silva, C., Alves, M.: Virtual fitting room augmented reality techniques for e-commerce. In: International Conference on ENTERprise Information Systems (CENTERIS), Part II: Communications in Computer and Information Science (CCIS 220), pp. 62–71. Springer, Berlin (2011)Google Scholar
  13. 13.
    Nasri, S., Behrad, A., Razzazi, F.: Spatio-temporal 3D surface matching for hand gesture recognition using ICP algorithm. Signal Image Video Process. 9, 1205–1220 (2013)CrossRefGoogle Scholar
  14. 14.
    Traumann, A., Anbarjafari, G., Escalera, S.: A new retexturing method for virtual fitting room using kinect 2 camera. In: Compute Vision and Pattern Recognition Conference, pp. 75–79. IEEE (2015)Google Scholar
  15. 15.
    Daneshmand, M., Aabloo, A., Anbarjafari, G.: Size-dictionary interpolation for robot’s adjustment. Front. Bioeng. Biotechnol. 3, 63 (2015)CrossRefGoogle Scholar
  16. 16.
    Volino, P., Magnenat-Thalmann, N.: The miracloth software. In: Virtual Clothing, pp. 231–259. Springer, Berlin (2000)Google Scholar
  17. 17.
    Carignan, M., Yang, Y., Thalmann, N.M., Thalmann, D.: Dressing animated synthetic actors with complex deformable clothes. SIGGRAPH Comput. Graph. 26(2), 99–104 (1992)Google Scholar
  18. 18.
    Protopsaltou, D., Luible, C., Arevalo, M., Magnenat-Thalmann, N.: A body and garment creation method for an internet based virtual fitting room. In: Advances in Modelling, Animation and Rendering, pp. 105–122. Springer, Berlin (2002)Google Scholar
  19. 19.
    Li, R., Zou, K., Xu, X., Li, Y., Li, Z.: Research of interactive 3d virtual fitting room on web environment. In: Fourth International Symposium on Computational Intelligence and Design (ISCID), 2011, vol. 1, pp. 32–35. IEEE (2011)Google Scholar
  20. 20.
    Nakamura, R., Izutsu, M., Hatakeyama, S.: Estimation method of clothes size for virtual fitting room with kinect sensor. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2013, pp. 3733–3738. IEEE (2013)Google Scholar
  21. 21.
    Wang, C.C., Wang, Y., Yuen, M.M.: Feature based 3d garment design through 2d sketches. Comput. Aided Des. 35(7), 659–672 (2003)CrossRefGoogle Scholar
  22. 22.
    Abels, A., Kruusmaa, M.: Shape control of an anthropomorphic tailoring robot mannequin. Int. J. Humanoid Robot. 10(2) (2013). doi: 10.1142/S0219843613500023
  23. 23.
    Kruusmaa, M., Abels, A.: Design of a shape-changing anthropomorphic mannequin for tailoring applications. In: International Conference on Advanced Robotics (ICAR), pp. 1–6. IEEE (2009)Google Scholar
  24. 24.
    Bogue, R., Loughlin, C., Loughlin, C.: Shape changing and self-reconfiguring robots. Ind. Robot Int. J. 42(4) (2015)Google Scholar
  25. 25.
    Cardou, P., Bouchard, S., Gosselin, C.: Kinematic-sensitivity indices for dimensionally nonhomogeneous jacobian matrices. IEEE Trans. Robot. 26(1), 166–173 (2010)CrossRefGoogle Scholar
  26. 26.
    Daneshmand, M., Saadatzi, M.H., Masouleh, M.T.: Kinematic sensitivity and workspace optimization of planar parallel mechanisms using evolutionary techniques. In: First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 384–389. IEEE (2013)Google Scholar
  27. 27.
    Altomonte, M., Zerbato, D., Botturi, D., Fiorini, P.: Simulation of deformable environment with haptic feedback on GPU. In: International Conference on Intelligent Robots and Systems (IROS), pp. 3959–3964. IEEE/RSJ (2008)Google Scholar
  28. 28.
    Zhang, M., Lu, Z.: Hybrid elastic registration using constrained free-form deformation. In: International Conference on Medical Image Analysis and Clinical Applications (MIACA), pp. 75–78. IEEE (2010)Google Scholar
  29. 29.
  30. 30.
    Guo, M., Kuzmichev, V.E., Adolphe, D.C.: Human-friendly design of virtual system “female body-dress”. Autex Res. J. 15(1), 19–29 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Morteza Daneshmand
    • 1
  • Alvo Aabloo
    • 1
  • Cagri Ozcinar
    • 2
    • 3
  • Gholamreza Anbarjafari
    • 1
  1. 1.iCV Group, Institute of TechnologyUniversity of TartuTartuEstonia
  2. 2.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  3. 3.Telecom ParisTechParisFrance

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