图像拼接(image stitching)是指将两张或更多的有重叠部分的影像,拼接成一张全景图或是高分辨率影像的技术。图像拼接有两大步骤:图像配准和图像融合

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作者简介: 艾海舟,清华大学计算机系教授,目前主要从事与人脸人体相关的计算机视觉方面的研究,在人脸及人体图像理解领域提出了一系列性能优越的算法,具有重要的学术价值和明显的应用价值。发表论文80余篇,授权国际专利1项,国际专利申请4项。

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最新论文

Image stitching is a classical and crucial technique in computer vision, which aims to generate the image with a wide field of view. The traditional methods heavily depend on the feature detection and require that scene features be dense and evenly distributed in the image, leading to varying ghosting effects and poor robustness. Learning methods usually suffer from fixed view and input size limitations, showing a lack of generalization ability on other real datasets. In this paper, we propose an image stitching learning framework, which consists of a large-baseline deep homography module and an edge-preserved deformation module. First, we propose a large-baseline deep homography module to estimate the accurate projective transformation between the reference image and the target image in different scales of features. After that, an edge-preserved deformation module is designed to learn the deformation rules of image stitching from edge to content, eliminating the ghosting effects as much as possible. In particular, the proposed learning framework can stitch images of arbitrary views and input sizes, thus contribute to a supervised deep image stitching method with excellent generalization capability in other real images. Experimental results demonstrate that our homography module significantly outperforms the existing deep homography methods in the large baseline scenes. In image stitching, our method is superior to the existing learning method and shows competitive performance with state-of-the-art traditional methods.

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