期刊文献+

Spectral Distance Distributions for Non-rigid Objects 被引量:1

Spectral Distance Distributions for Non-rigid Objects
下载PDF
导出
摘要 Non-rigid shape deformation without tearing or stretching is called isometry. There are many difficulties to research non-rigid shape in Euclidean space. Therefore, non-rigid shapes are firstly embedded into a none-Euclidean space. Spectral space is chosen in this paper. Then three descriptors are proposed based on three spectral distances. The existence of zero-eigenvalue has negative effects on computation of spectral distance, Therefore the spectral distance should be computed from the first non-zcro-eigenvalue. Experiments show that spectral distance distributions are very effective to describe the non-rigid shapes. Non-rigid shape deformation without tearing or stretching is called isometry. There are many difficulties to research non-rigid shape in Euclidean space. Therefore, non-rigid shapes are firstly embedded into a none-Euclidean space. Spectral space is chosen in this paper. Then three descriptors are proposed based on three spectral distances. The existence of zero-eigenvalue has negative effects on computation of spectral distance, Therefore the spectral distance should be computed from the first non-zcro-eigenvalue. Experiments show that spectral distance distributions are very effective to describe the non-rigid shapes.
出处 《Computer Aided Drafting,Design and Manufacturing》 2013年第2期17-24,共8页 计算机辅助绘图设计与制造(英文版)
基金 Partly Supported by NKBRPC(2004CB318006) NNSFC(60873164 and 60533090)
关键词 NON-RIGID shape analysis pattern recognization ISOMETRY Laplace-Beltrami operator SPECTRUM non-rigid shape analysis pattern recognization isometry Laplace-Beltrami operator spectrum
  • 相关文献

参考文献32

  • 1Hilaga M, Shinagawa Y, Komura T, Kun// T. Topology Matching for Fully Automatic Similarity Estimation of 3D Shapes [C] // Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 200 I: 203-212.
  • 2Hamza A, Krim H, Geodesic Object Representation and Recognition [C] // International Conference on Discrete Geometry for Computer Imagery, Naples, 2003, 2886: 378-387.
  • 3Elad A, Kimmel R. On Bending Invariant Signatures for Surfaces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 26(10): 1285-1295.
  • 4Bronstein A, Bronstein M, Kimmel R. Generalized Multidimensional Scaling: A Framework for Isometry-invariant Partial Surface Matching [C] // three spectral distance distributions after col?or-mapping. The darker the pixel, the more similar the two models is. Inversely, if a pixel is brighter, there are more distinctions between two models. Most of darkest pixels are clustering in the diagonal for Biharmonic distance distributions and Diffusion dis?tance distributions. However, there are a few chaoses in the confusion matrix of Commute-time distance distributions, which reflect the distribution curves in FigA. Proceedings of National Academy of Sciences (PNAS), 2006,103(5): 1168-1172.
  • 5Bronstein A, Bronstein M, Kimmel R. Numerical Geometry of Non-Rigid Shapes [M]. Springer, 2008.
  • 6Memoli F, SAPIRO G. A Theoretical and Computational Recognition of Point of Computational Framework for Isometry Invariant Cloud Data [J]. Foundations Mathematics, 2005, 5(3): 313-347.
  • 7Memoli F. On the Use of Gromov-hausdorff Distances for Shape Comparison [C] // Proceedings of EG/lEEE Symposium on Point Based Graphics 2007, 81-90.
  • 8Bronstein A, Bronstein M, Kimmel R. Efficient Computation of Isometry-invariant Distances Between Surfaces [J]. SIAM Journal of Scientific Computing, 2006, 28(5): 1812-1836.
  • 9Bronstein A, Bronstein M, Kimmel R, Mahmoudi M, Sapiro G. A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-robust Non-rigid Shape Matching [J]. International Journal of Computer Vision, 20 I 0, 89(2-3): 266-286.
  • 10Bronstein M, Bronstein A. Shape Recognition with Spectral Distances [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 //.

同被引文献2

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部