将经典分割算法改造为可训练的损失函数，提升图像分割精度。
Reimagining Image Segmentation using Active Contour: From Chan Vese Algorithm into a Proposal Novel Functional Loss Framework
- 基于水平集的主动轮廓思想，构建可微分的分割损失函数。
- 在多个公开数据集上优于传统损失函数，分割效果更优。
- 代码开源，适合研究分割损失或经典算法改进的开发者。
本文系统研究了图像分割中的Chan-Vese算法。通过对其能量泛函和基于水平集的偏微分方程进行离散化分析，给出了理论证明并用MATLAB实现。结合现代计算机视觉方法，提出一种基于主动轮廓的新型可训练分割损失函数，采用PyTorch.nn.ModuleLoss与水平集框架实现。在常见计算机视觉分割数据集上对比评估了经典损失函数与所提方法的性能。所有代码与材料均开源于https://github.com/gguzzy/chan_vese_functional_loss。
原文摘要 · Abstract (English)
In this paper, we present a comprehensive study and analysis of the Chan-Vese algorithm for image segmentation. We employ a discretized scheme derived from the empirical study of the Chan-Vese model's functional energy and its partial differential equation based on its level set function. We provide a proof of the results and an implementation using MATLAB. Leveraging modern computer vision methodologies, we propose a functional segmentation loss based on active contours, utilizing pytorch.nn.ModuleLoss and a level set based on the Chan-Vese algorithm. We compare our results with common computer vision segmentation datasets and evaluate the performance of classical loss functions against our proposed method. All code and materials used are available at https://github.com/gguzzy/chan_vese_functional_loss.
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。