A Pipeline and Metric for Validation of Personalized Human Body Models

Sukhraj Singh, Subodh Kumar

2017

Abstract

Advanced and personalized Human Body Models (HBM) are increasingly important in human centered industry design such as passive vehicular safety analysis, using finite element and other methods. Often accurate HBMs are painstakingly constructed for median human dimensions, and then modified and re-sized using personalization algorithms for various applications. Personalization algorithms rely on various anthropometric measurements, and sometimes manual intervention, to deform the median HBM. The quality of a personalized model is often defined in terms of local properties such as aspect ratio of finite elements produced. In some cases it is inferred by visual comparison with some ground truth model or by measuring the anthropometric errors with respect to known values. We seek to define the quality of deformation in anatomically suitable geometric terms, which can be automatically computed. To this end, we compare the deformed anatomical surface meshes with that of the median mesh in a shape descriptor space. Shape comparison and matching is a well studied area. The tools devised for the same are largely application dependent. We present pipeline and a metric for validating anatomical surface meshes. It is a problem that has not been extensively studied, even though general shape comparison and matching techniques abound. Our metric incorporates global and part based shape signatures. The main contribution of our work is to explore techniques suitable for comparison of anatomical meshes by non technical experts. We formulate a pipeline that needs minimal user intervention.

References

  1. Alliez, P., Pion, S., and Gupta, A. (2016). Principal component analysis. In CGAL User and Reference Manual. CGAL Editorial Board, 4.9 edition.
  2. Aspert, N., Santa-Cruz, D., and Ebrahimi, T. (2002). Mesh: measuring errors between surfaces using the hausdorff distance. In Multimedia and Expo, 2002. ICME 7802. Proceedings. 2002 IEEE International Conference on, volume 1, pages 705-708 vol.1.
  3. Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94:115-147.
  4. Cazals, F. and Pouget, M. (2005). Estimating differential quantities using polynomial fitting of osculating jets. Computer Aided Geometric Design, 22(2):121 - 146.
  5. CEESAR, INRIA, U. et al. (2014). Position and Personalize Advanced Human Body Models for Injury Prediction. http://www.piper-project.eu/. [Online; accessed 18-October-2016].
  6. Chen, X., Golovinskiy, A., and Funkhouser, T. (2009). A benchmark for 3d mesh segmentation. In ACM SIGGRAPH 2009 Papers, SIGGRAPH 7809, pages 73:1- 73:12, New York, NY, USA. ACM.
  7. Cheng, H., Obergefell, L., and Rizer, A. (1996). The development of the gebod program. In Biomedical Engineering Conference, 1996., Proceedings of the 1996 Fifteenth Southern, pages 251-254.
  8. Cignoni, P., Rocchini, C., and Scopigno, R. (1998). Metro: measuring error on simplified surfaces. Computer Graphics Forum, 17(2):167-174.
  9. Gal, R., Shamir, A., and Cohen-Or, D. (2007). Poseoblivious shape signature. IEEE Transactions on Visualization and Computer Graphics, 13(2):261-271.
  10. Gayzik, F. S., Moreno, D. P., Vavalle, N. A., Rhyne, A. C., and Stitzel, J. D. (2011). Development of the global human body models consortium mid-sized male full body model. Injury Biomechanics Research, pages 39-12.
  11. Gerkey, B. P. (2004). C Implementation of the Hungarian Method. http://robotics.stanford.edu/ gerkey/tools/hungarian.html. [Online; accessed 14-July2016].
  12. Gray, H. (2001). Anatomy of the human body. Philadelphia: Lea & Febiger, 1918; Bartleby.com, 2000. http://www.bartleby.com/107/17.html. [Online; accessed 18-October-2016].
  13. Hwang, E., Hallman, J., Klein, K., Rupp, J., Reed, M., and Hu, J. (2016). Rapid development of diverse human body models for crash simulations through mesh morphing. Technical report, SAE Technical Paper.
  14. Iwamoto, M., Kisanuki, Y., Watanabe, I., Furusu, K., Miki, K., and Hasegawa, J. (2002). Development of a finite element model of the total human model for safety (thums) and application to injury reconstruction. In Proceedings of the International Research Council on the Biomechanics of Injury conference, volume 30, pages 12-p. International Research Council on Biomechanics of Injury.
  15. Iyer, N., Jayanti, S., Lou, K., Kalyanaraman, Y., and Ramani, K. (2005). Three-dimensional shape searching: state-of-the-art review and future trends. ComputerAided Design, 37(5):509 - 530. Geometric Modeling and Processing 2004.
  16. Jiang, L., Zhang, X., and Zhang, G. (2013). Partial shape matching of 3d models based on the laplacebeltrami operator eigenfunction. Journal of Multimedia, 8(6):655-661.
  17. Koenderink, J. J. and van Doorn, A. J. (1992). Surface shape and curvature scales. Image and Vision Computing, 10(8):557 - 564.
  18. Lalonde, N. M., Petit, Y., Aubin, C.-E., Wagnac, E., and Arnoux, P.-J. (2013). Method to geometrically personalize a detailed finite-element model of the spine. IEEE Transactions on Biomedical Engineering, 60(7):2014-2021.
  19. Lavou, G. (2011). A multiscale metric for 3d mesh visual quality assessment. Computer Graphics Forum, 30(5):1427-1437.
  20. Lavou, G., Cheng, I., and Basu, A. (2013). Perceptual quality metrics for 3d meshes: Towards an optimal multiattribute computational model. In 2013 IEEE International Conference on Systems, Man, and Cybernetics, pages 3271-3276.
  21. Pouget, M. and Cazals, F. (2016). Estimation of local differential properties of point-sampled surfaces. In CGAL User and Reference Manual. CGAL Editorial Board, 4.9 edition.
  22. Poulard, D., Bermond, F., Dumas, R., Bruyere, K., and Compigne, S. (2012). Geometrical personalisation of human fe model using palpable markers on volunteers. Computer methods in biomechanics and biomedical engineering, 15(sup1):298-300.
  23. Robin, S. (2001). Humos: human model for safety-a joint effort towards the development of refined human-like car occupant models. In 17th international technical conference on the enhanced safety vehicle, page 297.
  24. Rubner, Y., Tomasi, C., and Guibas, L. J. (1998). A metric for distributions with applications to image databases. In Computer Vision, 1998. Sixth International Conference on, pages 59-66.
  25. Shapira, L., Shamir, A., and Cohen-Or, D. (2008). Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer, 24(4):249-259.
  26. Sidi, O., van Kaick, O., Kleiman, Y., Zhang, H., and CohenOr, D. (2011). Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Trans. on Graphics (Proc. SIGGRAPH Asia), 30(6):126:1-126:10.
  27. Siek, J. G., Lee, L.-Q., and Lumsdaine, A. (2001). Boost Graph Library: User Guide and Reference Manual, The. Pearson Education.
  28. Tangelder, J. W. H. and Veltkamp, R. C. (2007). A survey of content based 3d shape retrieval methods. Multimedia Tools and Applications, 39(3):441.
  29. Theologou, P., Pratikakis, I., and Theoharis, T. (2014). A review on 3d object retrieval methodologies using a part-based representation. Computer-Aided Design and Applications, 11(6):670-684.
  30. Vezin, P. and Verriest, J. P. (2005). Development of a set of numerical human models for safety. In 19th International Technical Conference on the Enhanced Safety of Vehicles, Washington DC, pages 6-9.
  31. Yaz, I. O. and Loriot, S. (2016). Triangulated surface mesh segmentation. In CGAL User and Reference Manual. CGAL Editorial Board, 4.9 edition.
  32. Zhou, L. and Pang, A. (2001). Metrics and visualization tools for surface mesh comparison. In Visual Data Exploration and Analysis VIII, 99 (May 3, 2001), volume 4302, pages 99-110.
Download


Paper Citation


in Harvard Style

Singh S. and Kumar S. (2017). A Pipeline and Metric for Validation of Personalized Human Body Models . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017) ISBN 978-989-758-224-0, pages 160-171. DOI: 10.5220/0006176201600171


in Bibtex Style

@conference{grapp17,
author={Sukhraj Singh and Subodh Kumar},
title={A Pipeline and Metric for Validation of Personalized Human Body Models},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)},
year={2017},
pages={160-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006176201600171},
isbn={978-989-758-224-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)
TI - A Pipeline and Metric for Validation of Personalized Human Body Models
SN - 978-989-758-224-0
AU - Singh S.
AU - Kumar S.
PY - 2017
SP - 160
EP - 171
DO - 10.5220/0006176201600171