Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Estimating Geometric Transformation of Planar Pattern in Spherical Panoramic Image

구면 파노라마 영상에서의 평면 패턴의 기하 변환 추정

  • Received : 2015.01.08
  • Accepted : 2015.06.29
  • Published : 2015.10.15
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Abstract

A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.

핀홀 카메라 모델을 가정하는 기존 영상처리 기술의 평면 대 평면 간 기하 변환은 구면 파노라마 영상에서의 픽셀 좌표에는 적용될 수 없다. 본 논문에서는 구면 파노라마 영상과 평면 영상의 특징점정합 쌍이 주어졌을 때 두 영상에 포함된 평면 기하 변환 관계를 추정하는 방법을 제안한다. 정합된 특징점들로부터 평면 패턴의 위도 변화, 경도 변화, 회전 변화, 크기 변화 인자를 모두 구하여 기하 변환을 추정하는 것이 본 논문에서 제안하는 방법의 목적이다. 평면 영상을 구면 파노라마 영상에 투영하게 될 경우 두 번의 비선형 좌표계 변환이 포함되어 기하 변환식이 복잡하다. 제안하는 방법은 좌표 변환뿐만 아니라 변환에 내재된 각 인자들을 모두 알아낼 수 있는 것이 장점이다. 실험 결과 제안하는 방법은 약 1%의 오차 수준에서 기하 변환을 추정하였고 위도 및 회전 등 주요 변형 요인에 영향을 거의 받지 않았다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. J. P. Snyder, "Flattening the Earth: Two Thousand Years of Map Projections," The University of Chicago Press, Chicago, Illinois, 1994.
  2. G. Fangi, C. Nardinocchi, "Photogrammetric processing of spherical panoramas," The Photogrammetric Record, Vol. 28(143), pp. 293-311, 2013. https://doi.org/10.1111/phor.12031
  3. Q.-H. Wang, F. Miao, B. Liu, C.-P. Li, "Panorama Image Distortion Correction Method based on Straight Baseline," International Conference on Apperceiving Computing and Intelligence Analysis, pp. 203-207, 2008.
  4. P. Hansen, P. Cork, W. Boles, K. Daniilidis, "Scale-Invariant Features on the Sphere," IEEE 11th International Conference on Computer Vision, pp. 1-8, 2007.
  5. P. Hansen, P. Cork, W. Boles, K. Daniilidis, "Scale invariant feature matching with wide angle images," IEEE/RSJ International Conference of Intelligent Robots and Systems, pp. 1689-1694, 2007.
  6. J. Cruz-Mota, I. Bogdanova, B. Paquier, M. Bierlaire, J.-P. Thiran, "Scale invariant feature transform on the sphere: Theory and applications," IJCV, Vol. 98(2), pp. 217-241, 2012. https://doi.org/10.1007/s11263-011-0505-4
  7. Z. Arican, P. Frossard, "Scale-invariant features and polar descriptors in omnidirectional imaging," IEEE International Conference on Image Processing, pp. 3505-3508, 2010.
  8. Z. Arican, P. Frossard, "OmniSIFT: Scale invariant features in omnidirectional images," IEEE International Conference on Image Processing, pp. 3505-3508, 2010.
  9. Z. Arican, P. Frossard, "Sampling-aware polar descriptors on the sphere," IEEE International Conference on Image Processing, pp. 3509-3512, 2010.
  10. D.-S. Ly, C. Demonceaux, R. Seulin, Y. Fougerolle, "Scale invariant line matching on the sphere," IEEE International Conference on Image Processing, pp. 3022-3025, 2013.
  11. G. Tong, R. Liu, J. Tan, "3D information retrieval in mobile robot vision based on spherical compound eye," IEEE International Conference on Robotics and Biomimetics, pp. 1895-1900, 2011.
  12. J. Carufel, R. Laganiere, "Matching cylindrical panorama sequences using planar reprojections," IEEE International Conference on Computer Vision Workshops, pp. 320-327, 2011.
  13. H. Bay, T. Tuytelaars, L.-V. Gool, "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, Vol. 110, pp. 404-417, 2006.
  14. G. Lowe, "Distinctive image features from scaleinvariant keypoints," International Journal of Computer Vision, Vol. 60, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. G. Tong, J. Gu, "Locating objects in spherical panoramic images," IEEE International Conference on Robotics and Biomimetics, pp. 818-823, 2011.
  16. Z. Zhou, B. Niu, C. Ke, W. Wu, "Static object tracking in road panoramic videos," IEEE International Symposium on Multimedia, pp. 57-64, 2010.