Natural Image Stitching with the Global Similarity Prior

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


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.


Image stitching Panoramas Image warping 

Supplementary material

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


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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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