SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching
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 appro
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National University of Singapore, Singapore, Singapore [email protected] Advanced Digital Sciences Center, Singapore, Singapore [email protected]
Abstract. 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.
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Introduction
Traditional image stitching techniques estimate a global 2D transformation (e.g. homography transformation) to align the input images [3,22,23]. The underlying assumption is that the images are taken at a fixed viewpoint or the scene is roughly planar. Violation of these assumptions will result in visual artifacts such as ghosting or misalignment that cannot be accounted for by a global 2D transformation. Such misalignment between the warped image and the reference image is referred to as parallax, and in this paper, we primarily want to address the problem of image stitching under large parallax. For images with small parallax, some spatially-varying warping methods [19,20,25] combined with advanced image composition techniques like seam cutting [2,15] and multi-band blending [4] usually suffice. However, when the images are taken from different viewpoints and the scene contains non-planar or discontinuous surfaces (often the case when the images are taken casually by users), most existing methods fail to produce satisfactory stitching results due to the presence of large parallax [26]. For images with large parallax, global alignment, Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46487-9 23) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part III, LNCS 9907, pp. 370–385, 2016. DOI: 10.1007/978-3-319-46487-9 23
Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching
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Fig. 1. Comparison of global alignment and our seam-guided local alignment. Left: Input images. Middle: Stitching result by APAP [25] (all features are used). Right: Stitching result by our method (only features around the final stitching seam are used).
as an over-simplified model to account for the underlying camera-scene geometry, cannot produce visually plausible stitching results (see Fig. 1). Instead, one only needs to find an alignment model that will produce good seams to stitch two images. The desiderata of a good seam is that it should either pass
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