基于卷积神经网络的鞋外底磨损模式学习
Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks
摘要：
随着时间的推移，鞋类外底获得了穿着它们的个人所特有的特性。法医科学家在很大程度上依靠他们通过多年经验获得的技能和知识来分析鞋印上的这些特征。在这项工作中，我们提出了一个卷积神经网络模型，该模型可以预测一个独特的鞋印数据集上的磨损模式，该数据集捕捉了一双鞋的寿命和磨损情况。我们提出了一个额外的架构，能够重建外底回到其原始状态在一个给定的一周，并提供实证评估这两种模式的性能。
英文摘要：
Footwear outsoles acquire characteristics unique to the individual wearing them over time. Forensic scientists largely rely on their skills and knowledge, gained through years of experience, to analyse such characteristics on a shoeprint. In this work, we present a convolutional neural network model that can predict the wear pattern on a unique dataset of shoeprints that captures the life and wear of a pair of shoes. We present an additional architecture able to reconstruct the outsole back to its original state on a given week, and provide empirical evaluations of the performance of both models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
