A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866916056197496832 |
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| author | Tettamanti, Horacio Guicciardi, Giulia Zanella, Mattia |
| author_facet | Tettamanti, Horacio Guicciardi, Giulia Zanella, Mattia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28619 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models Tettamanti, Horacio Guicciardi, Giulia Zanella, Mattia Computer Vision and Pattern Recognition Adaptation and Self-Organizing Systems 35L60, 35Q20, 35Q70, 35Q80, 68U10, 68U20 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. |
| title | A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models |
| topic | Computer Vision and Pattern Recognition Adaptation and Self-Organizing Systems 35L60, 35Q20, 35Q70, 35Q80, 68U10, 68U20 |
| url | https://arxiv.org/abs/2605.28619 |