Metraipolis › archive › dc_e27cddefdf9d

Modelling dynamic systems transfer functions from events in computational neuromorphic imaging — arXiv:2609.29863

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.29863
License (as archived): CC0-1.0
Archived: 2026-10-08T06:44:37.929Z
SHA-256 of the archived text: b89d3fb63aca39ba25ad691454403df3c73ecd12f6033186a46a5987a49a44a8
Stable URL of this page: https://metraipolis.com/documento/dc_e27cddefdf9d

How to cite

Modelling dynamic systems transfer functions from events in computational neuromorphic imaging — arXiv:2609.29863 — arXiv (en), https://arxiv.org/abs/2609.29863 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e27cddefdf9d (Metraipolis archive dc_e27cddefdf9d, sha256 b89d3fb63aca39ba25ad691454403df3c73ecd12f6033186a46a5987a49a44a8)

BibTeX

@misc{dce27cddefdf9d,
  title = {Modelling dynamic systems transfer functions from events in computational neuromorphic imaging — arXiv:2609.29863},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.29863},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e27cddefdf9d; sha256 b89d3fb63aca39ba25ad691454403df3c73ecd12f6033186a46a5987a49a44a8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.29863. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. The classical procedure for measuring a Point Spread Function (PSF), illuminating the system with a constant point source, therefore has no event-based equivalent: the probe must carry a temporal profile, and that profile becomes part of the measurement. A growing body of Computational Neuromorphic Imaging (CNI) work already exploits this, pairing engineered or modulated optics with event sensing, but each system adopts a particular excitation together with a particular reading of the event stream without the correspondence between the two being stated. We examine that correspondence directly within a analytical framework of an Linear Shift-Invariant (LSI) optical system with a specified Modulation Transfer Function (MTF), a first-order filter EVS pixel model, and three different temporal probes: a step function, a linear ramp and an exponential ramp. By analysing the inverse of the entire chain for different event-statistic, and comparing the results to the specified MTF, we identify the context where each probe is most relevant. We consider how photon-noise and cross-array threshold mismatch effects the analytical accuracy of the probe-inverse. Results show that the widely used step probe is highly susceptible to mismatch while resilient to photon shot-noise, while a linear rise probe and exponential rise probe retain their ability to infer signal levels even with high mismatch. We discuss the potential of dynamic-PSFs as components of a full forward operator from scene to events. In this, we use this analytical description to define dynamic-PSFs around EVS, and discuss the gaps toward a unified pixel model and a scene-composition framework required for CNI.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-25 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.29863 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T06:09:29.892Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).