Image Reassembly Combining Deep Learning and Shortest Path Problem
This addresses image reassembly for applications like archaeology or forensics, but appears incremental as it builds on existing deep learning and graph-based approaches.
The paper tackles the problem of reassembling images from disjointed fragments by developing deep neural architectures to predict fragment positions, which outperform previous state-of-the-art methods with concrete improvements, and casting the reassembly into a shortest path graph problem with new construction algorithms.
This 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.