发表机构
Université du Québec à Trois-Rivières（魁北克大学三河分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种不变切线角描述符和带状U-Net，用于二维碎片邻接预测，在合成数据上达到98%准确率，在真实基准上优于原始方法。
AI 中文摘要
本文解决了基于轮廓的二维碎片对之间邻接预测的问题。我们改进了Beaulac论文中提出的两阶段架构，该架构使用旋转等变的Siamese卷积神经网络对两个碎片的两条轮廓上的局部图像窗口对进行评分。评分被收集在一个邻接矩阵中，ResNet在该矩阵中检测揭示两个碎片邻接的部分反对角线带。在当前工作中，我们保留流程，并将局部评分替换为轮廓窗口的切线角分布的比较，这使得其通过构造对碎片旋转不变，并且对所选轮廓起点不敏感。这些调整可以是无需训练的似然比，也可以是在对应点上训练的小型一维卷积模型。我们还将最终分类器替换为带状U-Net，该网络对带进行分割并对碎片对进行分类，从而在决策的同时获得共享弧。在原始论文的合成数据集中，切线描述符在所有测试配置中的表现与图像窗口方法相当或更好。所提出的流程在评估修正后达到了98%的准确率，而原始方法为93%至95%。我们在仅使用合成数据训练的模型上，在PairingNet基准上测试了我们的流程，获得了0.93的AUC。此外，在PairingNet配对搜索协议条件下，我们的学习描述符在真实数据集上获得了0.82的Recall@10，而原始论文的最佳模型为0.56。
英文摘要
This paper addresses the prediction of adjacency between pairs of 2D fragments based on their contours. We improved the two-stage architecture proposed in Beaulac's thesis, in which a rotation-equivariant Siamese convolutional neural network scores pairs of local image windows along the two contours of two fragments. The scores are gathered in an adjacency matrix in which a ResNet detects the partial anti-diagonal band that reveals the adjacency of two fragments. In the current work, we keep the pipeline and replace the local score by a comparison of tangent-angle profiles of contour windows, making it, by construction, invariant to fragment rotation and agnostic to the selected contour-starting point. These adaptations may be either a training-free likelihood ratio or a small one-dimensional convolutional model trained on corresponding points. We also replaced the final classifier by a band U-Net that segments the band and classifies the pair, so that the shared arc is obtained along with the decision. In the synthetic data set of the original thesis, the tangent descriptor performs as well as or better than the image-window approach in all tested configurations. The proposed pipeline reaches an accuracy of 98%, vs 93% to 95% for the original approach once its evaluation is corrected. We tested our pipeline, with models trained only on synthetic data, on the PairingNet benchmark, and obtained an AUC of 0.93. Furthermore, under the PairingNet pair-searching protocol conditions, our learned descriptor obtains a Recall@10 of 0.82 on the real set against 0.56 from the best model of the original paper.
Comments14 pages, 4 figures, 6 tables
