用神经网络从视频中学习边界演化规律，可处理真实场景的噪声干扰。
Neural Networks for Threshold Dynamics Reconstruction
- 基于MBO算法和细胞自动机设计双模型，分别处理单一与多种动态演化
- 在冰融、火势蔓延等真实视频上实现边界重建与噪声下外推
- 支持跨动态泛化，适合需要实时演化的视觉建模任务
我们提出两种受Merriman-Bence-Osher（MBO）算法和细胞自动机启发的卷积神经网络架构，用于从视频数据中建模与学习阈值动态下的界面演化。第一种为单动态MBO网络，针对每段输入视频学习特定核函数与阈值，不适应新动态；第二种为元学习MBO网络，通过按输入自适应参数实现跨多种阈值动态的泛化。两种模型在合成与真实视频（冰融化、火势传播）上进行评估，性能指标显示其能有效重建并外推演变边界，即使在噪声条件下亦表现稳健。实验结果表明，两类网络在多样化合成与真实动态中均具强鲁棒性。
原文摘要 · Abstract (English)
We introduce two convolutional neural network (CNN) architectures, inspired by the Merriman-Bence-Osher (MBO) algorithm and by cellular automatons, to model and learn threshold dynamics for front evolution from video data. The first model, termed the (single-dynamics) MBO network, learns a specific kernel and threshold for each input video without adapting to new dynamics, while the second, a meta-learning MBO network, generalizes across diverse threshold dynamics by adapting its parameters per input. Both models are evaluated on synthetic and real-world videos (ice melting and fire front propagation), with performance metrics indicating effective reconstruction and extrapolation of evolving boundaries, even under noisy conditions. Empirical results highlight the robustness of both networks across varied synthetic and real-world dynamics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。