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New paper explores stability of scale-space metrics for image comparison · arXiv

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper on arXiv detailing the stability of scale-space metrics for comparing images. The study focuses on metrics derived from Gaussian scale-space representations, which have connections to Besov spaces and Wasserstein distances. The paper quantifies the metrics' resilience to geometric deformations and introduces rotationally-invariant versions for comparing tomographic projections. Additionally, it presents efficient algorithms for metric evaluation from finite samples and proves their robustness against additive noise, with findings illustrated by numerical experiments. AI

IMPACT This research contributes to foundational understanding in image analysis and metric stability, potentially impacting future AI models for computer vision tasks.

RANK_REASON The cluster contains a research paper published on arXiv.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New paper explores stability of scale-space metrics for image comparison · arXiv

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · William Leeb ·

    On the stability of scale-space metrics

    arXiv:2606.27605v1 Announce Type: cross Abstract: We study the stability of a classical family of metrics defined over functions' Gaussian scale-space representations, focusing on the comparison of images (functions of two variables). These metrics have precedents both in harmoni…

  2. arXiv cs.CV TIER_1 English(EN) · William Leeb ·

    On the stability of scale-space metrics

    We study the stability of a classical family of metrics defined over functions' Gaussian scale-space representations, focusing on the comparison of images (functions of two variables). These metrics have precedents both in harmonic analysis, specifically the theory of Besov space…

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