Robust Multi-homography Method for Image Stitching under Large Viewpoint Changes

Yan, Wuxia; Liu, Chuancai; Peng, Furong

doi:10.14257/ijhit.2017.10.9.01

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…The method can improve alignment accuracy, but for complex scenes with multiple planes, this method incorrectly divides the different planes into one structure, which will lead to alignment errors. Hence, Yan et al ( 2017 ) proposed a robust multi-homography image composition method. By calculating different homographies from different types of features, multiple homographies are then blended with Gaussian weights to construct a panorama.…”

Section: Related Workmentioning

confidence: 99%

An improved adaptive triangular mesh-based image warping method

2023

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It is of vital importance to stitch the two images into a panorama in many computer vision applications of motion detection and tracking and virtual reality, panoramic photography, and virtual tours. To preserve more local details and with few artifacts in panoramas, this article presents an improved mesh-based joint optimization image stitching model. Since the uniform vertices are usually used in mesh-based warps, we consider the matched feature points and uniform points as grid vertices to strengthen constraints on deformed vertices. Simultaneously, we define an improved energy function and add a color similarity term to perform the alignment. In addition to good alignment and minimal local distortion, a regularization parameter strategy of combining our method with an as-projective-as-possible (APAP) warp is introduced. Then, controlling the proportion of each part by calculating the distance between the vertex and the nearest matched feature point to the vertex. This ensures a more natural stitching effect in non-overlapping areas. A comprehensive evaluation shows that the proposed method achieves more accurate image stitching, with significantly reduced ghosting effects in the overlapping regions and more natural results in the other areas. The comparative experiments demonstrate that the proposed method outperforms the state-of-the-art image stitching warps and achieves higher precision panorama stitching and less distortion in the overlapping. The proposed algorithm illustrates great application potential in image stitching, which can achieve higher precision panoramic image stitching.

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Section: Related Workmentioning

confidence: 99%

An improved adaptive triangular mesh-based image warping method

2023

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“…In the following, the term 'non-rigid' stitching is used to refer to such methods, and the term 'rigid' to denote methods or transformations that do not incorporate arbitrary local deformations. In contrast to many previous non-rigid stitching algorithms (Castanheira de Souza et al, 2012;Yan et al, 2017;Yeung et al, 2008), the proposed method is readily suitable for stitching both 2D and 3D image mosaics. It is based on computing the local deformation field between the overlapping regions of the sub-images non-iteratively using (local) phase correlation (Kuglin and Hines, 1975;Tajima et al, 2012).…”

Section: Introductionmentioning

confidence: 97%

NRStitcher: non-rigid stitching of terapixel-scale volumetric images

et al. 2019

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Summary In modern microscopy, the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side-by-side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g. changes in the environment, damage caused by the radiation used to probe the sample or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed sub-images. The implementation of the method supports distributed computing and can be used to generate teravoxel-size mosaics. We demonstrate its capabilities by assembling a 5.6 teravoxel tomographic image mosaic of microvasculature in whole mouse brain. The method is compared to existing rigid stitching implementations designed for very large datasets, and observed to create artifact-free image mosaics in comparable runtime with the same hardware resources. Availability and implementation The stitching software and C++/Python source code are available at GitHub (https://github.com/arttumiettinen/pi2) along with an example dataset and user instructions.

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Robust Multi-homography Method for Image Stitching under Large Viewpoint Changes

Abstract: Abstract

Cited by 2 publications

References 23 publications

An improved adaptive triangular mesh-based image warping method

An improved adaptive triangular mesh-based image warping method

NRStitcher: non-rigid stitching of terapixel-scale volumetric images

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