Naturalness of warping is gaining extensive attention in image stitching. Recent warps such as SPHP, AANAP and GSP, use a global similarity to effectively mitigate projective distortion (which enlarges regions), however, they necessarily bring in perspective distortion (which generates inconsistency). In this paper, we propose a quasi-homography warp, which balances perspective distortion against projective distortion in the non-overlapping region, to create natural-looking mosaics. Our approach formulates the warp as a solution of a system of bivariate equations, where perspective distortion and projective distortion are characterized as slope preservation and scale linearization respectively. Our proposed warp only relies on a global homography thus is totally parameter-free. A comprehensive experiment shows that quasi-homography outperforms some state-of-the-art warps in urban scenes, including homography, AutoStitch and SPHP. A user study demonstrates that quasi-homography wins most users' favor as well, comparing to homography and SPHP.
Other methods combine image alignment with seam-cutting approaches @cite_9 @cite_31 @cite_27 @cite_12 , to find a locally registered area which is seamlessly blended instead of aligning the overlapping region globally. Gao @cite_34 proposed a seam-driven framework, which searches a homography with minimal seam costs instead of minimal alignment errors on a set of feature correspondences. Zhang and Liu @cite_41 proposed a parallax-tolerant warp, which combines homography and content-preserving warps to locally register images. Lin @cite_24 proposed a seam-guided local alignment warp, which iteratively improves the warp by adaptive feature weighting according to the distance to current seams.
@cite_41 @cite_9 @cite_24 @cite_27 @cite_31 @cite_34 @cite_12
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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. We describe an algorithm for generating panoramic video from unstructured camera arrays. Artifact-free panorama stitching is impeded by parallax between input views. Common strategies such as multi-level blending or minimum energy seams produce seamless results on quasi-static input. However, on video input these approaches introduce noticeable visual artifacts due to lack of global temporal and spatial coherence. In this paper we extend the basic concept of local warping for parallax removal. Firstly, we introduce an error measure with increased sensitivity to stitching artifacts in regions with pronounced structure. Using this measure, our method efficiently finds an optimal ordering of pair-wise warps for robust stitching with minimal parallax artifacts. Weighted extrapolation of warps in non-overlap regions ensures temporal stability, while at the same time avoiding visual discontinuities around transitions between views. Remaining global deformation introduced by the warps is spread over the entire panorama domain using constrained relaxation, while staying as close as possible to the original input views. In combination, these contributions form the first system for spatiotemporally stable panoramic video stitching from unstructured camera array input. For wide-angle images with heavy lens distortion, especially those widely used in multi-camera systems, the single global homography cannot satisfy the required warp due to nonlinear distortions, leading to misalignment and shape distortion. This paper proposes a multi-homography warping to stitch wide-angle images with unknown lens distortion, which integrates multiple local homographies with a global homography for accurate alignment and shape preservation. We suggest a solution by conditional sampling to obtain a larger proportion of inliers for more accurate estimation of local and global projective transformations. We introduce an adaptive weighting scheme to combine these transformations for smoothing our warp over the entire target image from the local homographies to the global homography. The experiments evaluate the alignment accuracy and shape preservation of the proposed method. The aim of this paper is to achieve seamless image stitching without producing visual artifact caused by severe intensity discrepancy and structure misalignment, given that the input images are roughly aligned or globally registered. Our new approach is based on structure deformation and propagation for achieving the overall consistency in image structure and intensity. The new stitching algorithm, which has found applications in image compositing, image blending, and intensity correction, consists of the following main processes. Depending on the compatibility and distinctiveness of the 2D features detected in the image plane, single or double optimal partitions are computed subject to the constraints of intensity coherence and structure continuity. Afterwards, specific 1D features are detected along the computed optimal partitions from which a set of sparse deformation vectors is derived to encode 1D feature matching between the partitions. These sparse deformation cues are robustly propagated into the input images by solving the associated minimization problem in gradient domain, thus providing a uniform framework for the simultaneous alignment of image structure and intensity. We present results in general image compositing and blending in order to show the effectiveness of our method in producing seamless stitching results from complex input images.