Seamline network generation based on foreground segmentation for orthoimage mosaicking

Li, Li; Tu, Jingmin; Gong, Ya‐Jun; Yao, Jian; Li, Jie

doi:10.1016/j.isprsjprs.2018.12.002

Cited by 22 publications

(8 citation statements)

References 26 publications

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“…Fast and robust seam estimation (FARSE) [25] searches for seams by defining the gray weighted distance and differential gradient domain as the difference cost. Li et al [36] designed a two-image stitching method based on foreground segmentation. A. Eden et al [37] proposed a two-step optimal seam algorithm, which can stitch the image smoothly even if there is scene motion and alignment error.…”

Section: Related Workmentioning

confidence: 99%

UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

Chen

Peng

et al. 2022

Remote Sensing

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This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect.

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Section: Related Workmentioning

confidence: 99%

UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

Chen

Peng

et al. 2022

Remote Sensing

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“…For remote sensing images, Li et al. [29–31] solved the optimal seamline by setting different ways of image difference measures combined with graph cuts method. Li et al.…”

Section: Introductionmentioning

confidence: 99%

“…Lin et al [28] used the estimated seamline to guide the optimization process of local alignment, incorporating a curve and line structure-preserving warping method, in order to improve the seamline quality in each iteration. For remote sensing images, Li et al [29][30][31] solved the optimal seamline by setting different ways of image difference measures combined with graph cuts method. Li et al [32] obtained the optimal seamline by solving the minimum of the target energy function based on graph cuts.…”

Section: Introductionmentioning

confidence: 99%

Research on seamless image stitching based on fast marching method

Pan,

Li,

et al. 2023

IET Image Processing

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Image stitching is an important way to achieve large‐field high‐resolution imaging. The inconsistencies in brightness and structure and defects in ghosting, blurring and misalignment between images, which are inevitable and difficult to eliminate, make a challenge to image stitching, due to the external lighting environment and changes in camera pose and parameters. Here, a novel method is proposed to search for the optimal seamline based on the fast marching method, which can stitch large parallax images with high quality. A feature weight map is first formed based on the similarity in colour, edge, texture and saliency of the images. Then it is used as the cost value of the seamline to search for the optimal seamline by fast marching method. The results show that this new method is more efficient to reduce defects, such as ghosting, misalignment and chromatic aberration, and realize high quality image stitching compared with traditional stitching tools and methods, which provides a new perspective for image stitching technology.

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“…Image mosaicking is a series of multiple algorithms used to assemble various images of the same scene into one single image with a larger field of view. In addition, mosaicking is used as a tool in many fields to attain a certain result such as robotic‐assisted minimally invasive surgery [2], motion detection and tracking [3], geo‐referencing [4], environment perception for robots [5], aerial mosaicking [6], satellite/orthoimage mosaicking [7], augmented reality [8], and much more.…”

Section: Introductionmentioning

confidence: 99%