Smoothly varying affine stitching

Lin, Wen-Yan; Liu, Siying; Matsushita, Yasuyuki; Ng, Tian-Tsong; Cheong, Loong-Fah

doi:10.1109/cvpr.2011.5995314

Cited by 235 publications

(151 citation statements)

References 21 publications

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“…For addressing the model inadequacy of global warps and improving alignment accuracy, recently, several local warp models have been proposed, such as the smoothly varying affine (SVA) warp [9] and the as-projective-aspossible (APAP) warp [13]. For better alignment accuracy, rather than relying on a single global warp, these methods adopt multiple local parametric warps on the overlapping regions to account for misalignment.…”

Section: Introductionmentioning

confidence: 99%

Shape-Preserving Half-Projective Warps for Image Stitching

Chang

Sato

Chuang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

284

214

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This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation of the overlapping regions into the non-overlapping regions and the resultant warp gradually changes from projective to similarity across the image. The proposed warp has the strengths of both projective and similarity warps. It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps. It can also be combined with more advanced local-warp-based alignment methods such as the as-projective-as-possible warp for better alignment accuracy. With the proposed warp, the field of view can be extended by stitching images with less projective distortion (stretched shapes and enlarged sizes).

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Section: Introductionmentioning

confidence: 99%

Shape-Preserving Half-Projective Warps for Image Stitching

Chang

Sato

Chuang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

284

214

View full text Add to dashboard Cite

show abstract

“…These two interpolation methods cannot obtain satisfactory results [8], because the shape interpolation and image deformation are usually done by affine interpolation on a triangle. When there is a projective transformation relationship between two images, these methods cannot yield satisfactory projective results.…”

Section: Defects Of a Directly Projected Homography Matrixmentioning

confidence: 99%

“…Because their wrappings are based on projective transformations without improvement, the results suffer from parallax error caused by projection. Lin et al [8] proposed a smoothly varying affine stitching method to deal with images. However, their main idea is still a projective transformation, and the results have parallax error.…”

Section: Introductionmentioning

confidence: 99%

Image Stitching Method Based on Projective Interpolation

Zhang¹,

Ren²,

Huang³

et al. 2016

International Journal of Robotics and Automation

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Parallax error and ghosting are serious problems in unmanned aerial vehicle (UAV) image stitching. These problems occur even more obviously when stitching together large-parallax images. In this paper, an image stitching method based on projective interpolation is proposed. First, we estimate the homography matrix from the UAV images, obtain its inverse matrix. Second, the two matrices are decomposed and interpolated into sub-matrices. Then these submatrices are composed into two new matrices. Finally, the image stitching is finished by warping images using the new matrices.The proposed method disperses the parallax into the two images to help reduce the parallax error. Moreover, the method introduces a seam cutting technique to preserve the geometric structure and further improve local alignment. We conducted three experiments that compared the proposed method with other state-of-the-art methods. The experimental results show that the proposed method can effectively reduce parallax error, eliminate ghosting, and achieve an overall good stitching result.

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“…Lin et al developed a smoothly varying affine stitching method to handle parallax [13]. Zaragoza et al developed a technique to compute an asprojective-as-possible warping that aims to be globally projective while allowing local non-projective deviations to account for parallax [27].…”

Section: Related Workmentioning

confidence: 99%

“…Recent image stitching methods use spatially-varying warping algorithms to align input images [13,27]. While spatially-varying warping can better handle parallax than homography, it still cannot work well on images with large parallax.…”

Section: Introductionmentioning

confidence: 99%