4D Polygonal Approximation of the Skeleton for 3D Object Decomposition
Luca Serino, Carlo Arcelli, Gabriella Sanniti di Baja
2013
Abstract
We improve a method to decompose a 3D object into parts (called kernels, simple-regions and bumps) starting from the partition of the distance labeled skeleton into components (called complex-sets, simple-curves and single-points). In particular, each simple-curve of the partition is here interpreted as a curve in a 4D space, where the coordinates of each point are related to the three spatial coordinates of the corresponding voxel of the 3D simple-curve and to its associated distance label. Then, a split type polygonal approximation method is employed to subdivide, in the limits of the adopted tolerance, each curve in the 4D space into straight-line segments. Vertices found in the 4D curve are used to identify corresponding vertices in the 3D simple-curve. The skeleton partition is then used to recover the parts into which the object is decomposed. Finally, region merging is taken into account to obtain a decomposition of the object more in accordance with human intuition.
References
- C. Arcelli, G. Sanniti di Baja, L. Serino, 2011. Distance driven skeletonization in voxel images, IEEE Trans. PAMI, 33, 709-720.
- H. Blum, 1973. Biological shape and visual science, J. Theor. Biol., 38, 205-287.
- G. Borgefors, 1996. On digital distance transform in three dimensions, CVIU, 64, 368-376.
- Z-Q. Cheng, B. Li, G. Dang, S-Y. Jin, 2008. Meaningful Mesh Segmentation Guided by the 3D Short-Cut Rule, Proc. AGMP 2008, LNCS 4975, 244-257, Springer.
- F.de Goes, S. Goldenstein, L. Velho, 2008. A Hierarchical Segmentation of Articulated Bodies, Computer Graphics Forum, 27, 1349-1356.
- D. D. Hoffman, W. A. Richards, 1984. Parts of Recognition, Cognition, 18, 65-96.
- D. Macrini, K. Siddiqi, S. Dickinson, 2008. From Skeletons to Bone Graphs: Medial Abstraction for Object Recognition, Proc. CVPR 2008, 1-8.
- U. Ramer, 1972. An Iterative procedure for the polygonal approximation of plane curves, CGIP, 1, 244-256.
- L. Serino, G. Sanniti di Baja, C. Arcelli, 2010. Object decomposition via curvilinear skeleton partition, Proc. ICPR 2010, 4081-4084, IEEE CS Press.
- L. Serino, G. Sanniti di Baja, C. Arcelli, 2011. “Using the skeleton for 3D object decomposition”, in A. Heyden and F. Kahl (Eds.): SCIA 2011, LNCS 6688, 447-456, Springer.
- A. Shamir, 2008. A Survey on Mesh Segmentation Techniques, Computer Graphics Forum, 27, 1539- 1556.
- P. Shilane, P. Min, M. Kazhdan, T. Funkhouser, 2004. The Princeton Shape Benchmark, Shape Modeling International, Genova, Italy.
- M. Singh, G. D. Seyranian, D. D. Hoffman, 1999. Parsing Silhouettes: the Short-Cut Rule, Perception&Psychophysics, 61, 636-660.
- S. Svensson, G. Sanniti di Baja, 2002. Using distance transforms to decompose 3D discrete objects, IMAVIS, 20, 529-540.
Paper Citation
in Harvard Style
Serino L., Arcelli C. and Sanniti di Baja G. (2013). 4D Polygonal Approximation of the Skeleton for 3D Object Decomposition . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 467-472. DOI: 10.5220/0004265204670472
in Bibtex Style
@conference{icpram13,
author={Luca Serino and Carlo Arcelli and Gabriella Sanniti di Baja},
title={4D Polygonal Approximation of the Skeleton for 3D Object Decomposition},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={467-472},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004265204670472},
isbn={978-989-8565-41-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - 4D Polygonal Approximation of the Skeleton for 3D Object Decomposition
SN - 978-989-8565-41-9
AU - Serino L.
AU - Arcelli C.
AU - Sanniti di Baja G.
PY - 2013
SP - 467
EP - 472
DO - 10.5220/0004265204670472