Abstract Low-textured image stitching remains a challenging problem. It is difficult to achieve good alignment of images and it is easy to break images structures are often broken due to insufficient and unreliable point correspondences. Moreover, because of the viewpoint variations between multiple images, the stitched images suffer from projective distortions. To solve these problems, this paper presents a line-guided local warping method with a global similarity constraint for image stitching. Line features which serve well for geometric descriptions and scene constraints, are employed to guide image stitching accurately. On one hand, the line features are integrated into a local warping model through a designed weight function. On the other hand, line features are adopted to impose strong geometric constraints, including line correspondence and line colinearity, to improve the stitching performance through mesh optimization. To mitigate projective distortions, we adopt a global similarity constraint, which is integrated with the projective warps via a designed weight strategy. This constraint causes the final warp to slowly change from a projective to a similarity transformation across the image. Finally, the images undergo a two-stage alignment scheme that provides accurate alignment and reduces projective distortion. We evaluate our method on a series of images and compare it with several other methods. The experimental results demonstrate that the proposed method provides a convincing stitching performance and that it outperforms other state-of-the-art methods.
Numerous studies have been devoted to image stitching; a comprehensive survey can be found in @cite_23 . The global homography model @cite_4 works well for planar scenes or for scenes acquired with parallax-free camera motion, but violation of these assumptions may lead to ghosting artifacts.
@cite_4 @cite_23
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This paper presents a parallax-robust video stitching technique for timely synchronized surveillance video. An efficient two-stage video stitching procedure is proposed in this paper to build wide Field-of-View (FOV) videos for surveillance applications. In the stitching model calculation stage, we develop a layered warping algorithm to align the background scenes, which is location-dependent and turned out to be more robust to parallax than the traditional global projective warping methods. On the selective seam updating stage, we propose a change-detection based optimal seam selection approach to avert ghosting and artifacts caused by moving foregrounds. Experimental results demonstrate that our procedure can efficiently stitch multi-view videos into a wide FOV video output without ghosting and noticeable seams. 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. 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. In this paper, we extend image stitching to video stitching for videos that are captured for the same scene simultaneously by multiple moving cameras. In practice, videos captured under this circumstance often appear shaky. Directly applying image stitching methods for shaking videos often suffers from strong spatial and temporal artifacts. To solve this problem, we propose a unified framework in which video stitching and stabilization are performed jointly. Specifically, our system takes several overlapping videos as inputs. We estimate both inter motions (between different videos) and intra motions (between neighboring frames within a video). Then, we solve an optimal virtual 2D camera path from all original paths. An enlarged field of view along the virtual path is finally obtained by a space-temporal optimization that takes both inter and intra motions into consideration. Two important components of this optimization are that: 1) a grid-based tracking method is designed for an improved robustness, which produces features that are distributed evenly within and across multiple views and 2) a mesh-based motion model is adopted for the handling of the scene parallax. Some experimental results are provided to demonstrate the effectiveness of our approach on various consumer-level videos and a Plugin, named “Video Stitcher” is developed at Adobe After Effects CC2015 to show the processed videos.