Spatially-Varying Image Warps for Scene Alignment

Chang, Che-Han; Chen, Chiu-Ju; Chuang, Yung-Yu

doi:10.1109/icpr.2014.21

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…This section describes the adoption of mesh optimization as the second step of the two-stage alignment scheme to further improve the performance of image stitching. Content-preserving warping is a mesh-based warping method that was first used for video stabilization in [10] and, later, successfully applied to image stitching [25][26][27]. It is well-suited for small local adjustments.…”

Section: Alignment Refinement With Line Constraintsmentioning

confidence: 99%

Image stitching by line-guided local warping with global similarity constraint

Xiang

Xia

Bai

et al. 2018

Pattern Recognition

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Low-textured image stitching remains a challenging problem. It is difficult to achieve good alignment and it is easy to break image structures 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.

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Section: Alignment Refinement With Line Constraintsmentioning

confidence: 99%

Image stitching by line-guided local warping with global similarity constraint

Xiang

Xia

Bai

et al. 2018

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Shape preserving half projective (SPHP) warping method [9] spatially employed projective transformation for overlapping regions and similarity transformation for non-overlapping regions. Chang et al [10] combined the pixel-based method and feature-based method for image stitching, and followed by a shape preserving aggregation. Mesh warping was used to parameterize image stitching to alleviate distortion problem.…”

Section: Related Workmentioning

confidence: 99%