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Learning to Align Images using Weak Geometric Supervision
[article]
2018
arXiv
pre-print
Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences. However, the cost of creating such datasets is usually prohibitive. In this paper, we propose a new approach to align two images related by an unknown 2D homography where the local descriptor is learned from scratch from the images and the homography is
arXiv:1808.01424v1
fatcat:brkoe2ah3ncmpjxdiomzapcg3q