A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models
This work provides a novel theoretical framework for image segmentation, but its practical impact is limited to domain-specific applications.
The paper introduces a multiscale kinetic framework for image segmentation, modeling pixels as interacting particles, and derives a macroscopic model for efficient segmentation. Numerical tests demonstrate effectiveness and robustness under noise.
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.