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A Dominant Feature based Nomalization and Relational Description of Shape Signature for Scale/Rotational Robustness

2차원 형상 변화에 강건한 지배적 특징 기반 형상 시그너쳐의 정규화 및 관계 특징 기술

  • Song, Ho-Geun (Dept. of Computer&Information Engineering, Hanseo University) ;
  • Koo, Ha-Sung (Dept. of Computer&Information Engineering, Hanseo University)
  • 송호근 (한서대학교 컴퓨터정보공학과) ;
  • 구하성 (한서대학교 컴퓨터정보공학과)
  • Received : 2011.10.22
  • Accepted : 2011.11.09
  • Published : 2011.11.30

Abstract

In this paper, we propose a Geometrical Centroid Contour Distance(GCCD) which is described by shape signature based on contour sequence. The proposed method uses geomertrical relation features instead of the absolute angle based features after it was normalized and aligned with dominant feature of the shape. Experimental result with MPEG-7 CE-Shape-1 Data Set reveals that our method has low time/spatial complexity and scale/rotation robustness than the other methods, showing that the precision of our method is more accurate than the conventional desctiptors. However, performance of the GCCD is limited with concave and complex shaped objects.

본 논문에서는 윤곽선의 순열을 기반으로 하여 형상 시그너쳐를 추출하고, 주도적 특징을 기반으로 형상 정열 및 정규화한 뒤, 기존 절대각도기반 관계성질 대신 기하 관계성질을 이용하는 새로운 형상 기술자, 기하학적 중심거리값(Geometrical Centroid Contour Distance : GCCD)을 제안하였다. 비교 실험을 통하여 제안 방식은 기존 기술자들에 비해 시간 복잡도와 공간 복잡도가 작아 실시간 처리에 유리하면서, 2차원 객체의 크기와 회전 변화에 강건함을 보였다. 또한 MPEG-7 표준 영상을 대상으로 한 정확도 실험을 통하여 다양한 영상군에 대한 비교를 수행하여 제안 방식이 기존 방식들보다 높은 정확도를 보임을 확인하였다. 하지만 제안 방식은 볼록형 객체에 대한 높은 정확도를 보이는 반면 오목형이면서 복잡한 형상을 가지는 객체에 대하여는 형상 변별력이 제한된다는 것도 알게 되었다.

Keywords

References

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