A Robust Method for Automatic Panoramic UAV Image Mosaic

Chen, Jun; Xu, Quan; Luo, Lei; Wang, Yongtao; Wang, Shuchun

doi:10.3390/s19081898

Cited by 23 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The local deformability and alignment capability are strong and this method can locally adaptively adjust the image, achieve precise local alignment, and maintain global affinity. However, the use of affine regularization is appropriate for interpolation, which may not be optimal for extrapolation, and the affinity is insufficient for completing the perspective transformation [30]. Zaragoza et al [13] proposed as-projective-aspossible deformation model.…”

Section: B Description Of Deformation Modelmentioning

confidence: 99%

UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

Chen

Luo

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural-looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in three dimensions is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multi-region guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.

show abstract

Section: B Description Of Deformation Modelmentioning

confidence: 99%

UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

Chen

Luo

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar to DHW, this method is only suitable for specific scenes. Chen et al introduced a nonrigid matching algorithm based on vector field consensus (VFC) [ 28 ] to the mosaic system for generating accurate feature matching [ 29 ]. In most methods other than global warping, the model fitted by RANSAC and the model used for alignment are relatively independent.…”

Section: Related Workmentioning

confidence: 99%

Image Stitching Based on Nonrigid Warping for Urban Scene

Deng

Yuan

Deng

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators.

show abstract

“…In this step, the transformation homography matrix is widely used because it is simple and effective, as UAV image stitching mainly addresses stitching planar scenes [22]. However, to obtain more accurate results, some literatures [17,[23][24][25][26] proposed more complex models and effectively removed parallax.…”

Section: Related Workmentioning

confidence: 99%

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Shao

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.

show abstract

A Robust Method for Automatic Panoramic UAV Image Mosaic

Cited by 23 publications

References 54 publications

UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

Image Stitching Based on Nonrigid Warping for Urban Scene

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Contact Info

Product

Resources

About