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A view-free image stitching network based on global homography.
- Source :
-
Journal of Visual Communication & Image Representation . Nov2020, Vol. 73, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- Image stitching is a traditional but challenging computer vision task, aiming to obtain a seamless panoramic image. Recently, researchers begin to study the image stitching task using deep learning. However, the existing learning methods assume a relatively fixed view during the image capturing, thus show a poor generalization ability to flexible view cases. To address the above problem, we present a cascaded view-free image stitching network based on a global homography. This novel image stitching network does not have any restriction on the view of images and it can be implemented in three stages. In particular, we first estimate a global homography between two input images from different views. And then we propose a structure stitching layer to obtain the coarse stitching result using the global homography. In the last stage, we design a content revision network to eliminate ghosting effects and refine the content of the stitching result. To enable efficient learning on various views, we also present a method to generate synthetic datasets for network training. Experimental results demonstrate that our method can achieve almost 100% elimination of artifacts in overlapping areas at the cost of acceptable slight distortions in non-overlapping areas, compared with traditional methods. In addition, the proposed method is view-free and more robust especially in a scene where feature points are difficult to detect. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 73
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 147318744
- Full Text :
- https://doi.org/10.1016/j.jvcir.2020.102950