Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Correction of Missing Feature Points for 3D Modeling from 2D object images

2차원 객체 영상의 3차원 모델링을 위한 손실 특징점 보정

  • Received : 2015.10.15
  • Accepted : 2015.11.09
  • Published : 2015.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

다수의 2차원 객체 영상으로부터 3차원 형상을 복원하는 방법은 컴퓨터 비젼 분야에서 널리 연구되고 있다. 복원된 3차원 형상의 정확도 개선을 위해서는 잡음 영향을 줄이거나 영상 프레임 수를 확보하는 것이 무엇보다 중요하다. 그렇지만 특징점 추정 시 잡음은 잠재적으로 내포되고, 관측행렬을 구성하는 영상 프레임 수는 특징점 추적 실패, 장애요소 또는 낮은 해상력 등에 의해 일반적으로 감소하게 된다. 그래서 잠음 환경 하에 손실된 특징점을 보다 정확히 보정하여 사용 가능한 영상 프레임 수를 확보하는 것이 필수적이다. 따라서 우리는 잡음 분포 하에서 기하학적 특성을 이용해 손실 특징점의 오차 거리와 방향을 직접 제어할 수 있는 분석적 접근방법을 제안한다. 제안한 방법의 우수성은 합성과 실제 객체에 대한 실험 결과를 통해서 검증한다.

Keywords

References

  1. M. Irani, P. Anandan, "Factorization with Uncertainty," in Proceeding of European Conference on Computer Vision, Dublin, Ireland, pp. 539-553, 2000.
  2. R. Szeliski and S. B. Kang, "Shape Ambiguities in Structure-from-Motion," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.19. pp. 506-512, 1997. https://doi.org/10.1109/34.589211
  3. Tomasi and Kanade "Shape and Motion from Image Streams under Orthography: A factorization method," International Journal of Computer Vision, Vol.9, No.2, pp. 137-154, 1992. https://doi.org/10.1007/BF00129684
  4. Jacobs, "Linear Fitting with Missing Data for Structurefrom- Motion," in Proceeding of Computer Vision and Image Understanding, Vol.82, pp. 57-81, 2001.
  5. F. C. Guerreiro and M. Q. Aguiar, "Estimation of Rank Deficient Matrices from Partial Observations: Two-Step Iterative Algorithms," in Proceeding of Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 450-466, 2003.
  6. H. Shum, K. Ikeuchi, and R. Reddy, "Principal Component Analysis with Missing Data and Its Applications to Polyhedral Object Modeling," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.17, No.9, pp. 854-867, 1995. https://doi.org/10.1109/34.406651
  7. S. Soatto, and P. Perona, "Dynamic Rigid Motion Estimation from Weak Perspective," International Conference on Computer Vision, pp. 321-328, 1995.
  8. P. McLauchlan, I. Reid, D. Murray, "Recursive Affine Structure and Motion from Image Sequence," in Proceeding of European Conference on Computer Vision, pp. 217-224, 1994.
  9. R. Hartly, "Euclidean Reconstruction from Uncalibrated View," in Proceeding of 2nd European-US Workshop on Invariance, pp. 237-256, 1993.
  10. S. S. Koh, T.T. Zin, H. Hama, "Analysis of Geometrical Relations of 2D Affine-Projection Images and Its 3D Shape Reconstruction," The Institute of Electronics Engineers of Korea - Signal Processing, vol.44, no.4, pp.1-7, Jul. 2007.