论文标题
深嘴：用深度学习和最肖尔特路径优化解决视觉拼图拼图
Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning andShortest Path Optimization
论文作者
论文摘要
我们以碎片之间的空间来解决图像重新组装问题，以使模式和颜色连续性大多是无法使用的方式。间距模拟了考古碎片所遭受的侵蚀。我们裁剪片段边界，以迫使我们的算法从碎片的内容中学习。我们还通过删除片段和添加其他来源的碎片来使图像重新组装复杂。我们使用两步方法来获得重新组件：1）神经网络可以预测片段的位置，尽管它们之间存在差距； 2）通过这些预测进行最佳重新组件的图。在本文中，我们特别研究了在重新组件图中分支切割的效果。我们还提供了与文献的比较，求解复杂的图像重新组件，详细探讨数据集并提出适合其特异性的新指标。 关键字：图像重新组装，拼图拼图，深度学习，图形，分支机构，文化遗产
We tackle the image reassembly problem with wide space between the fragments, in such a way that the patterns and colors continuity is mostly unusable. The spacing emulates the erosion of which the archaeological fragments suffer. We crop-square the fragments borders to compel our algorithm to learn from the content of the fragments. We also complicate the image reassembly by removing fragments and adding pieces from other sources. We use a two-step method to obtain the reassemblies: 1) a neural network predicts the positions of the fragments despite the gaps between them; 2) a graph that leads to the best reassemblies is made from these predictions. In this paper, we notably investigate the effect of branch-cut in the graph of reassemblies. We also provide a comparison with the literature, solve complex images reassemblies, explore at length the dataset, and propose a new metric that suits its specificities. Keywords: image reassembly, jigsaw puzzle, deep learning, graph, branch-cut, cultural heritage
