SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching

  • Kaimo LinEmail author
  • Nianjuan Jiang
  • Loong-Fah Cheong
  • Minh Do
  • Jiangbo Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9907)


Image stitching with large parallax is a challenging problem. Global alignment usually introduces noticeable artifacts. A common strategy is to perform partial alignment to facilitate the search for a good seam for stitching. Different from existing approaches where the seam estimation process is performed sequentially after alignment, we explicitly use the estimated seam to guide the process of optimizing local alignment so that the seam quality gets improved over each iteration. Furthermore, a novel structure-preserving warping method is introduced to preserve salient curve and line structures during the warping. These measures substantially improve the effectiveness of our method in dealing with a wide range of challenging images with large parallax.


Target Image Feature Match Mesh Vertex Warping Model Image Stitching 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Singapore PSF grant 1321202075 and the HCCS research grant at the ADSC from Singapore’s Agency for Science, Technology and Research (A*STAR) (This work was partly done when Kaimo was interning in ADSC.).

Supplementary material

419975_1_En_23_MOESM1_ESM.pdf (30.4 mb)
Supplementary material 1 (pdf 31173 KB)


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kaimo Lin
    • 1
    • 2
    Email author
  • Nianjuan Jiang
    • 2
  • Loong-Fah Cheong
    • 1
  • Minh Do
    • 2
  • Jiangbo Lu
    • 2
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Advanced Digital Sciences CenterSingaporeSingapore

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