Advertisement

Application Research of Ripley’s K-function in Point Cloud Data Shape Analysis

  • Linlin TangEmail author
  • Xupeng Tong
  • Jingyong Su
Conference paper
  • 28 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)

Abstract

Classification and recognition of objects for images is an important issue in many scientific fields such as computer vision, biometrics and medical image analysis. An important feature of many objects is shape, so shape analysis has become an important part of classification. One method of shape analysis is to estimate boundaries and analyze the shape of these boundaries to determining the characteristics of the original object. However, many literature studies on point cloud shape analysis are based on existing shapes. This paper mainly refers to Ripley’s K-function in spatial point analysis, through this judgment on spatial distribution of point cloud data to determine the existence of shape in point cloud data, through the spatial distribution of 2D point cloud data and 3D point cloud data. Judging by the randomness of the experiment, K-function has a considerable effect on judging existence of point cloud data shape through relevant experimental verification analysis.

Keywords

Point cloud data Ripley’s K-function Spatial randomness 

Notes

Acknowledgement

This work was supported by Shenzhen Science and Technology Plan Fundamental Research Funding JCYJ20180306171938767 and Shenzhen Foundational Research Funding JCYJ20180507183527919.

References

  1. 1.
    Ji, S., Ren, Y., Ji, Z., et al.: An improved method for registration of point cloud. Opt.-Int. J. Light Electron Opt. 140, 451–458 (2017)CrossRefGoogle Scholar
  2. 2.
    Leal, N., Leal, E., German, S.T.: A linear programming approach for 3D point cloud simplification. IAENG Int. J. Comput. Sci. 44(1), 60–67 (2017)Google Scholar
  3. 3.
    Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (ToG) 32(3), 61–70 (2013)CrossRefGoogle Scholar
  4. 4.
    Srivastava, A., Jermyn, I.H.: Looking for shapes in two-dimensional cluttered point clouds. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1616–1629 (2009)CrossRefGoogle Scholar
  5. 5.
    Su, J., Srivastava, A., Huffer, F.W.: Detection, classification and estimation of individual shapes in 2D and 3D point clouds. Comput. Stat. Data Anal. 58, 227–241 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    de Oliveira Martins, L., da Silva, E.C., Silva, A.C., et al.: Classification of breast masses in mammogram images using Ripley’s K-function and support vector machine. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 784–794. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Qi, C.R., Su, H., Mo, K., et al.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)Google Scholar
  8. 8.
    Dixon, P.M.: Ripley’s K-function. Wiley StatsRef: Statistics Reference Online (2014)Google Scholar
  9. 9.
    Qi, C.R., Yi, L., Su, H., et al.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099–5108 (2017)Google Scholar
  10. 10.
    Arbia, G., Espa, G., Giuliani, D., et al.: Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function. Spat. Econ. Anal. 12(2–3), 326–346 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyShenzhenChina

Personalised recommendations