-
Image Reassembly Combining Deep Learning and Shortest Path Problem
AbstractThis paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that outperform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Paumard_2018_ECCV,
author = {Paumard, Marie-Morgane and Picard, David and Tabia, Hedi},
title = {Image Reassembly Combining Deep Learning and Shortest Path Problem},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
ECCV 2018 papers
The ECCV 2018 papers, provided here by the Computer Vision Foundation, are the author-created versions. The content of the papers is identical to the content of the officially published ECCV 2018 LNCS version of the papers as available on SpringerLink: https://link.springer.com/conference/eccv.
Intellectual property rights, copyright and all rights therein are retained by authors, by Springer as the publisher of the official ECCV 2018 proceedings or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.