Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Algorithm for improving the position of vanishing point using multiple images and homography matrix

다중 영상과 호모그래피 행렬을 이용한 소실점 위치 향상 알고리즘

  • Received : 2018.11.19
  • Accepted : 2019.01.04
  • Published : 2019.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose vanishing-point position-improvement algorithms by using multiple images and a homography matrix. Vanishing points can be detected from a single image, but the positions of detected vanishing points can be improved if we adjust their positions by using information from multiple images. More accurate indoor space information detection is possible through vanishing points with improved positional accuracy. To adjust a position, we take three images and detect the information, detect the homography matrix between the walls of the images, and convert the vanishing point positions using the detected homography. Finally, we find an optimal position among the converted vanishing points and improve the vanishing point position. The experimental results compared an existing algorithm and the proposed algorithm. With the proposed algorithm, we confirmed that the error angle to the vanishing point position was reduced by about 1.62%, and more accurate vanishing point detection was possible. In addition, we can confirm that the layout detected by using improved vanishing points through the proposed algorithm is more accurate than the result from the existing algorithm.

본 논문은 다중 영상과 호모그래피 행렬을 통해 소실점 위치의 정확도를 향상시키는 알고리즘을 제안한다. 단일 영상만을 활용하여 소실점 검출이 가능하지만, 여러 영상의 정보를 활용하여 소실점의 위치를 보정하면 소실점 위치의 정확도를 더 향상시킬 수 있다. 위치 정확도가 향상된 소실점을 통해 더 정확한 실내공간 정보 검출이 가능하다. 이를 위해 본 논문에서는 3개의 영상을 입력받아 정보를 검출한 후 영상의 벽면 간의 호모그래피 행렬을 검출하고, 검출된 호모그래피를 이용하여 소실점의 위치를 변환한다. 최종적으로 변환된 소실점 중 최적의 위치에 있는 소실점을 찾아내어 소실점 위치를 보정 함으로써 소실점 위치의 정확도를 향상시킨다. 실험 결과를 통해 기존의 알고리즘과 제안하는 알고리즘의 정확도를 비교 분석한다. 제안하는 알고리즘을 통해 소실점 위치에 대한 오차 각도가 약 1.62% 감소함을 확인하였고, 이를 통해 더 정밀한 소실점 검출이 가능하였다. 또한, 제안한 알고리즘을 통해 향상된 소실점을 이용하여 검출한 레이아웃이 기존 알고리즘의 결과에 비교해 더 정확한 것을 확인 할 수 있었다.

Keywords

SHGSCZ_2019_v20n1_477_f0001.png 이미지

Fig. 1. Overall process

SHGSCZ_2019_v20n1_477_f0002.png 이미지

Fig. 2. Relationship between line segment and cross point

SHGSCZ_2019_v20n1_477_f0003.png 이미지

Fig. 3. Image plane and three vanishing points

SHGSCZ_2019_v20n1_477_f0004.png 이미지

Fig. 4. Example of transformation using homography matrix (a) Matching (b) Transformation

SHGSCZ_2019_v20n1_477_f0005.png 이미지

Fig. 5. Matching wall information of two images

SHGSCZ_2019_v20n1_477_f0006.png 이미지

Fig. 6. Process of improving with the proposed algorithm

SHGSCZ_2019_v20n1_477_f0007.png 이미지

Fig. 7. Comparison of the result

Table 1. Average error angle of existing and proposed algorithm

SHGSCZ_2019_v20n1_477_t0001.png 이미지

References

  1. E. Delage, H. Lee, and A. Y. Ng, "A dynamic Bayesian network model for autonomous 3D reconstruction from a single indoor image,", pp.2418-2428, Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, IEEE, 2006. DOI: http://doi.org/10.1109/CVPR.2006.23
  2. H. Wang, S. Gould, and D. Koller, "Discriminative learning with latent variables for cluttered indoor scene understanding," Communications of the ACM, Vol.56, No.4, pp.92-99, 2013. DOI: http://doi.org/10.1145/2436256.2436276
  3. D. C. Lee, M. Hebert, and T. Kanade, "Geometric reasoning for single image structure recovery," pp.2136-2143, Computer Vision and Pattern Recognition, 2009, IEEE Conference on, IEEE, 2009. DOI: http://doi.org/10.1109/CVPR.2009.5206872
  4. V. Hedau, D. Hoiem, and D. Forsyth, "Recovering the spatial layout of cluttered rooms," pp.1849-1856, Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009. DOI: http://doi.org/10.1109/ICCV.2009.5459411
  5. T. Shao, W. Xu, K. Zhou, J. Wang, D. Li, and B. Guo, "An Interactive Approach to Semantic Modeling of Indoor Scenes with an RGBD Camera," ACM Transactions on Graphics, Vol.31, No.6, Article 136, 2012. DOI: http://doi.org/10.1145/2366145.2366155
  6. S. Choi, Q. Zhou, and V. Koltun, "Robust Reconstruction of Indoor Scenes," pp.5556-5565, 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015. DOI: http://doi.org/10.1109/CVPR.2015.7299195
  7. S. Jung, C. Lee, and H. Choi, "Method for structural vanishing point detection using orthogonality on single image," Korean Society for Internet Information, Vol.18, No.5, pp.39-46, 2017. DOI: http://dx.doi.org/10.7472/jksii.2017.18.5.39
  8. Y. Cha, D. Lee, B. Kim, and S. Jung, "Robust Vanishing Points Detection from Multiple Images," Korea Information Science Society, Vol.31, No.1, pp.745-747, 2004. DOI: http://www.dbpia.co.kr/Journal/ArticleDetail/NOD E00629007
  9. J. M. Coughlan, A.l. Yuille, "Manhattan World: Compass Direction from a Single Image by Bayesian Inference," pp.941-947, Seventh IEEE International Conference on Computer Vision,1999. DOI: http://doi.org/10.1109/ICCV.1999.790349
  10. R. Szeliski, "Image alignment and stitching: A tutorial," Technical Report MSR-TR-2004-92, Microsoft Research, 2004. DOI: https://www.microsoft.com/en-us/research/publication/image-alignment-and-stitching-a-tutorial/
  11. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: an efficient alternative to SIFT or SURF," pp.2564-2571, 2011 IEEE International Conference on Computer Vision, 2011. DOI: http://doi.org/10.1109/ICCV.2011.6126544
  12. M. A. Fischler, and R. C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Communications of the ACM, Vol.24, No.6, pp.381-395, 1981. DOI: http://doi.org/10.1145/358669.358692