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Natural Image Stitching with the Global Similarity Prior

  • Yu-Sheng Chen
  • Yung-Yu Chuang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9909)

Abstract

This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for each image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.

Keywords

Image stitching Panoramas Image warping 

Supplementary material

419978_1_En_12_MOESM1_ESM.pdf (23.8 mb)
Supplementary material 1 (pdf 24365 KB)

References

  1. 1.
    Brown, M., Lowe, D.G.: Recognising panoramas. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, ICCV 2003, vol. 2, pp. 1218–1225 (2003)Google Scholar
  2. 2.
    Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)CrossRefGoogle Scholar
  3. 3.
    Carroll, R., Agrawal, M., Agarwala, A.: Optimizing content-preserving projections for wide-angle images. Int. J. Comput. Vis. 28(3), 43 (2009)Google Scholar
  4. 4.
    Chang, C.H., Sato, Y., Chuang, Y.Y.: Shape-preserving half-projective warps for image stitching. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 3254–3261 (2014)Google Scholar
  5. 5.
    Gao, J., Kim, S.J., Brown, M.S.: Constructing image panoramas using dual-homography warping. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 49–56 (2011)Google Scholar
  6. 6.
    Grompone von Gioi, R., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a line segment detector. Image Process. On Line 2, 35–55 (2012)CrossRefGoogle Scholar
  7. 7.
    He, K., Chang, H., Sun, J.: Rectangling panoramic images via warping. ACM Trans. Graph. 32(4), 79:1–79:10 (2013)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Igarashi, T., Igarashi, Y.: Implementing as-rigid-as-possible shape manipulation and surface flattening. J. Graph., GPU, & Game Tools 14(1), 17–30 (2009)CrossRefGoogle Scholar
  9. 9.
    Kopf, J., Lischinski, D., Deussen, O., Cohen-Or, D., Cohen, M.: Locally adapted projections to reduce panorama distortions. Int. J. Comput. Vis. 28(4), 1083–1089 (2009)Google Scholar
  10. 10.
    Lezama, J., Grompone von Gioi, R., Randall, G., Morel, J.M.: Finding vanishing points via point alignments in image primal and dual domains. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014Google Scholar
  11. 11.
    Lin, C., Pankanti, S., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 1155–1163 (2015)Google Scholar
  12. 12.
    Lin, W.Y., Liu, S., Matsushita, Y., Ng, T.T., Cheong, L.F.: Smoothly varying affine stitching. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 345–352 (2011)Google Scholar
  13. 13.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Nomura, Y., Zhang, L., Nayar, S.K.: Scene collages and flexible camera arrays. In: Proceedings of the 18th Eurographics Conference on Rendering Techniques, EGSR 2007, pp. 127–138 (2007)Google Scholar
  15. 15.
    Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. In: ACM SIGGRAPH 2006 Papers, SIGGRAPH 2006, pp. 533–540 (2006)Google Scholar
  16. 16.
    Shum, H.Y., Szeliski, R.: Panoramic image mosaics. Technical Report MSR-TR-97-23, Microsoft Research, SeptemberGoogle Scholar
  17. 17.
    Szeliski, R.: Image alignment and stitching: a tutorial. Int. J. Comput. Vis. 2(1), 1–104 (2006)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Szeliski, R., Shum, H.Y.: Creating full view panoramic image mosaics and environment maps. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997, pp. 251–258 (1997)Google Scholar
  19. 19.
    Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM International Conference on Multimedia, MM 2010, pp. 1469–1472 (2010)Google Scholar
  20. 20.
    Zaragoza, J., Chin, T.J., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving DLT. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 2339–2346 (2013)Google Scholar
  21. 21.
    Zaragoza, J., Chin, T.J., Tran, Q.H., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving DLT. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1285–1298 (2014)CrossRefGoogle Scholar
  22. 22.
    Zelnik-Manor, L., Peters, G., Perona, P.: Squaring the circle in panoramas. In: Proceedings of ICCV 2005, vol. 2, pp. 1292–1299 (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan

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