用粒子系统模拟像素，实现高效图像分割。
A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models
- 将像素视为可交互的粒子，同步更新位置与颜色特征。
- 通过尺度变换得到简化宏观模型，准确描述像素分布演化。
- 适合处理含噪图像，对复杂场景分割效果好。
本文提出一种多尺度动能框架用于共识式图像分割。将图像视为相互作用粒子系统，每个像素由空间位置和编码颜色信息的内部特征表征。引入耦合交互机制，控制粒子在位置与特征空间中的演化，推导出结合传输、聚集与扩散效应的粒子密度动力学模型。通过适当的尺度化处理，获得一阶宏观模型，描述具有特定特征的像素比例随时间的变化。基于此简化模型，提出一种数据驱动的粒子优化方法，实现高精度图像分割。数值实验验证了该框架的有效性及在不同噪声条件下的鲁棒性。
原文摘要 · Abstract (English)
In this work, we present a multiscale kinetic framework for consensus-based image segmentation. By interpreting an image as a system of interacting particles, each pixel is characterised by its spatial position and an internal feature encoding color information. We introduce a coupled interaction scheme governing the evolution of particles in both position and feature spaces, from which we derive a kinetic formulation for the particle density in the space-feature domain combining transport, aggregation, and diffusion effects. Furthermore, through a suitable scaling, we obtain a first-order macroscopic model describing the evolution of the fraction of pixels carrying information on the fraction of pixels having a certain feature. Based on this reduced-complexity model, we present a data-oriented approach where we make use of particle-based optimisation techniques for the accurate segmentation of images. Numerical tests show the effectiveness of the proposed framework and its robustness under different noise conditions.
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。