DOI QR코드

DOI QR Code

Shape similarity measure for M:N areal object pairs using the Zernike moment descriptor

저니키 모멘트 서술자를 이용한 M:N 면 객체 쌍의 형상 유사도 측정

  • Huh, Yong (The Hong Kong Polytechnic Univ. Dept. of LSGI Professional Research Fellow) ;
  • Yu, Ki-Yun
  • Received : 2012.03.05
  • Accepted : 2012.04.21
  • Published : 2012.04.30

Abstract

In this paper, we propose a new shape similarity measure for M:N polygon pairs regardless of different object cardinalities in the pairs. The proposed method compares the projections of two shape functions onto Zernike polynomial basis functions, where the shape functions were obtained from each overall region of objects, thus not being affected by the cardinalities of object pairs. Moments with low-order basis functions describe global shape properties and those with high-order basis functions describe local shape properties. Therefore several moments up to a certain order where the original shapes were similarly reconstructed can efficiently describe the shape properties thus be used for shape comparison. The proposed method was applied for the building objects in the New address digital map and a car navigation map of Seoul area. Comparing to an overlapping ratio method, the proposed method's similarity is more robust to object cardinality.

본 연구는 저니키 모멘트 서술자를 이용하여 객체 쌍의 기수성에 영향을 받지 않고 M:N 면 객체 쌍의 형상 유사도를 측정할 수 있는 방법을 제안한다. 제안된 형상 유사도는 저니키 기저함수에 객체 집합의 공간적 분포 영역을 투영하여 얻어지는 모멘트를 이용하기 때문에 형상을 구성하는 객체들의 기수성에 영향을 받지 않는다. 또한 낮은 차수의 기저함수에 대응되는 모멘트는 전역적인 형상을 표현하고, 높은 차수의 기저함수에 대응되는 모멘트는 지역적인 형상을 표현하기 때문에 원형상과 유사한 수준으로 형상을 복원할 수 있는 차수까지의 모멘트를 이용함으로써 효과적으로 형상을 서술하고 비교하는 것이 가능하다. 제안된 방법은 서울시 지역의 도로명주소 지도와 차량용 항법 지도의 건물 객체를 대상으로 적용 및 평가하였다. 기존 중첩면적비를 이용한 유사도에 비하여 제안된 유사도는 기수성의 변화에 강건함을 확인할 수 있었다.

Keywords

References

  1. 최창수 (2011), 회전 불변 Zernike 모멘트를 이용한 홍채 인식 기법, 박사학위논문, 충북대학교, pp. 15-16.
  2. Arkin, E.M., Chew. L.P., Huttenlocher, D.P., Kedem, K. and Mitchell, J.S.B. (1991), An efficiently computable metric for comparing polygonal shapes, IEEE Transaction on PAMI, Vol. 13, No. 3, pp. 209-215. https://doi.org/10.1109/34.75509
  3. Butenuth, M., von Gosseln, G., Tiedge, M., Heipke, C., Lipeck, U., and Sester, M. (2007), Integration of heterogeneous geospatial data in a federated database. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, No. 5, pp. 328- 346. https://doi.org/10.1016/j.isprsjprs.2007.04.003
  4. Duda, R. O., Hart, P. E., and Stork, D. G. (2001), Pattern Classification, Wiley.
  5. Huang, L., Wang, S., Ye, Y., Wang, B. and Wu, L. (2010), Feature matching in cadastral map integration with a case study of Beijing, Proceedings of 2010 18th International Conference on Geoinformatics, Peking University, Beijing, China, pp. 1-4.
  6. Huh, Y., Yu, K., Heo, J. (2011), Detecting conjugate-point pairs for map alignment between two polygon datasets. Environment and Urban Systems, Vol. 35, No. 3, pp. 250-262. https://doi.org/10.1016/j.compenvurbsys.2010.08.001
  7. Khotanzad, A. and Hong, Y.H. (1990), Invariant image recognition by Zernike moments, IEEE transactions of pattern analysis and machine intelligence, Vol. 12, No. 5, pp. 489-497. https://doi.org/10.1109/34.55109
  8. Li, L. and Goodchild, M. (2011), An optimization model for linear feature matching in geographical data conflation. International Journal of Image and Data Fusion, Vol. 2, No. 4, pp. 309-328. https://doi.org/10.1080/19479832.2011.577458
  9. Min, D. Zhilin, L. and Xiaoyong, C. (2007), Extended Hausdorff distance for spatial objects in GIS, International Journal of Geographical Information Science, Vol. 21, No. 4, pp. 459- 475. https://doi.org/10.1080/13658810601073315
  10. OGC (2008), Loosely coupled synchronization of geographic databases in the canadian geospatial data infrastructure pilot, OpenGIS Discussion Paper(OGC 08-001).
  11. Revell, P. and Antoine, B. (2009), Automated matching of building features of differing levels of detail: A case study, Proceedings of International Cartography Conference 2009, Santiago, Chile.
  12. Samal, A., Seth, S. and Cueto, K. (2004), A feature-based approach to conflation of geospatial sources, International Journal of Geographical Information science, Vol. 18, No. 5, pp. 459-489. https://doi.org/10.1080/13658810410001658076
  13. Spaccapietra, S., Vangenot, C., Parent, C. and Zimanyi, E. (1999), MurMur: A Research Agenda on Multiple Representations. Proceedings of International Symposium on Database Applications in Non-Traditional Environments, Kyoto, Japan. pp. 373-384.
  14. Tan, P. N., Steinbach, M. and Kumar, V. (2005), Introduction to Data Mining, Addison-Wesley, pp. 500-501.
  15. Teh, C.H. and Chin, R.T. (1988), On image analysis by the methods of moments, IEEE transactions of pattern analysis and machine intelligence, Vol. 10, No. 4, pp. 496-497. https://doi.org/10.1109/34.3913
  16. Wenjing, T., Yanling, H., Yuxin, Z. and Ning, L. (2008), Research on areal feature matching algorithm based on spatial similarity, Proceedings of Control and Decision Conference 2008, Yantai, China, pp. 3326-3330.
  17. Yuan, S. and Tao, C. (1999), Development of conflation components, Proceedings of the International Conference on Geoinformatics and Socioinformatics, Ann Arbor, Michigan, USA, pp. 1-13.
  18. Zhang, D. and Lu, G. (2003), Evaluation of MPEG-7 shape descriptors against other shape descriptors. Multimedia Systems, Vol. 9, No. 1, pp. 15-30. https://doi.org/10.1007/s00530-002-0075-y
  19. Zhao, D., Sheng, Y. and Guo, H. (2008), An algorithm for automatically matching corresponding points on homonymous map features. Proceedings of Geoinformatics 2008 and Joint Conference on GIS and Built Environment. SPIE Guangzhou, China, pp. 1-10.

Cited by

  1. 이종의 공간 데이터 셋에서 매칭 객체 판별을 위한 임계값 산출 vol.31, pp.1, 2012, https://doi.org/10.7848/ksgpc.2013.31.1.23
  2. 필지 객체의 형상 정합을 이용한 건물 설계도면의 좌표 등록 vol.31, pp.3, 2012, https://doi.org/10.7848/ksgpc.2013.31.3.193
  3. 폭풍해일 침수예상도 검증을 위한 형상유사도 분석 : 형상기준 vol.12, pp.3, 2019, https://doi.org/10.21729/ksds.2019.12.3.13