Abstract
In this paper we present a method for the automatic processing of scanned human body data consisting of an algorithm for the extraction of curve skeletons of the 3D models acquired and a procedure for the automatic segmentation of skeleton branches. Models used in our experiments are obtained with a whole-body scanner based on structured light (Breuckmann bodySCAN, owned by the Faculty of Exercise and Sport Science of the University of Verona), providing triangulated meshes that are then preprocessed in order to remove holes and create clean watertight surfaces. Curve skeletons are then extracted with a novel technique based on voxel coding and active contours driven by a distance map and vector flow. The skeleton-based segmentation is based on a hierarchical search of feature points along the skeleton tree.
Our method is able to obtain on the curve skeleton a pose-independent subdivision of the main parts of the human body (trunk, head-neck region and partitioned limbs) that can be extended to the mesh surface and internal volume and can be exploited to estimate the pose and to locate more easily anthropometric features.
The curve skeleton algorithm applied allows control on the number of branches extracted and on the resolution of the volume discretization, so the procedure could be then repeated on subparts in order to refine the segmentation and build more complex hierarchical models.
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References
van Wijk, J.J., Telea, A.: An augmented fast. marching method for computing skeletons and centerlines. In: Proc. IEEE VisSym 2002, pp. 251–260. ACM Press, New York (2002)
Reniers, D., Telea, A.: Skeleton-based hierarchical shape segmentation. In: Proc. of the IEEE Int. Conf. on Shape Modeling and Applications (SMI), pp. 179–188 (2007)
Reniers, D., Van Wijk, J.J., Telea, A.: Computing multiscale curve and surface skeletons of genus 0 shapes using a global importance measure. IEEE Transactions on Visualization and Computer Graphics 14(2), 355–368 (2008)
Dey, T.K., Sun, J.: Defining and computing curve-skeletons with medial geodesic function. In: SGP 2006: Proceedings of the fourth Eurographics symposium on Geometry processing, Aire-la-Ville, Switzerland, pp. 143–152. Eurographics Association (2006)
Giachetti, A., Zanetti, G.: Aquatics reconstruction software: The design of a diagnostic tool based on computer vision algorithms. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds.) CVAMIA/MMBIA 2004. LNCS, vol. 3117, pp. 48–63. Springer, Heidelberg (2004)
Ju, T.: Robust repair of polygonal models. In: Proc. of ACM SIGGRAPH 2004, pp. 888–895. ACM Press, New York (2004)
Mortara, M., Patané, G., Spagnuolo, M.: From geometric to semantic human body models. Computers & Graphics, 185–196 (2006)
Moschini, D., Fusiello, A.: Tracking stick figures with hierarchical articulated icp. In: Proceedings THEMIS 2008, pp. 61–68 (2008)
Cornea, N., Silver, D., Yuan, X., Balasubramanian, R.: Computing hierarchical curve-skeleton of 3d objects. The Visual Computer (11), 945–955 (2005)
Cornea, N., Silver, D., Yuan, X., Balasubramanian, R.: Curve-skeleton applications. In: IEEE Visualization, pp. 95–102 (2005)
Gagvani, N., Silver, D.: Parameter-controlled volume thinning. Graph. Models and Image Proc. (3), 149–164 (1999)
Shapira, L., Shamir, A., Cohen-Or, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis. Comput. 24(4), 249–259 (2008)
Sharf, A., Lewiner, T., Shamir, A., Kobbelt, L.: On-the-fly curve-skeleton computation for 3d shapes. The Visual Computer 26(3) (2007)
Veltkamp, R.C., ter Haar, F.B.: Shrec 2007 3d retrieval contest. Technical Report UU-CS-2007-015, Department of Information and Computing Sciences (2007)
Werghi, N.: A robust approach for constructing a graph representation of articulated and tubular-like objects from 3d scattered data. Pattern Recognition Letters 27, 643–651 (2007)
Werghi, N.: Segmentation and modeling of full human body shape from 3-d scan data: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C 37(6), 1122–1136 (2007)
Xiao, Y., Siebert, P., Werghi, N.: A discrete reeb graph approach for the segmentation of human body scans. In: Proceedings of Fourth International Conference on3-D Digital Imaging and Modeling, 2003. 3DIM 2003, pp. 378–385 (2003)
Zhou, Y., Toga, A.: Efficient skeletonization of volumetric objects. TVCG (3), 196–209 (1999)
Yu, Y., Wang, Z., Xia, S., Mao, T.: Automatic joints extraction of scanned human body. HCI (12), 286–293 (2007)
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Lovato, C., Castellani, U., Giachetti, A. (2009). Automatic Segmentation of Scanned Human Body Using Curve Skeleton Analysis. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics CollaborationTechniques. MIRAGE 2009. Lecture Notes in Computer Science, vol 5496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01811-4_4
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DOI: https://doi.org/10.1007/978-3-642-01811-4_4
Publisher Name: Springer, Berlin, Heidelberg
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