XRF-to-Optical Field-of-View Localization with Vision Language Models
arXiv:2608.18309v1 Announce Type: new Abstract: Registering images acquired with different microscopy modalities is essential for relating complementary measurements of the same specimen. In correlative X-ray fluorescence (XRF) and optical microscopy, the XRF map often covers only a small region of an optical image acquired from the same or an adjacent tissue section. Field-of-view (FOV) localization is necessary but can be difficult when appearance and structure differ across modalities. Here we evaluate training-free vision language model (VLM) localization on two datasets representing same-section high-correspondence and adjacent-section low-correspondence imaging. We test unconstrained and metadata-constrained search and compare VLMs with geometric controls, classical template matching, and two alternative training-free approaches (DINOv2 and multiGradICON). Direct VLM prompting produced content-dependent spatial signals but was not reliable alone. Classical matching was most accurate when cross-modal structure was preserved but failed in the low-correspondence collection. A proposal-and-verify workflow used repeated VLM predictions as candidates and image-based similarity to select the final location. This workflow recovered useful localization in the low-correspondence regime.
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[Submitted on 18 Aug 2026]
Title:XRF-to-Optical Field-of-View Localization with Vision Language Models
View a PDF of the paper titled XRF-to-Optical Field-of-View Localization with Vision Language Models, by Xiangyu Yin and 9 other authors
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Abstract:Registering images acquired with different microscopy modalities is essential for relating complementary measurements of the same specimen. In correlative X-ray fluorescence (XRF) and optical microscopy, the XRF map often covers only a small region of an optical image acquired from the same or an adjacent tissue section. Field-of-view (FOV) localization is necessary but can be difficult when appearance and structure differ across modalities. Here we evaluate training-free vision language model (VLM) localization on two datasets representing same-section high-correspondence and adjacent-section low-correspondence imaging. We test unconstrained and metadata-constrained search and compare VLMs with geometric controls, classical template matching, and two alternative training-free approaches (DINOv2 and multiGradICON). Direct VLM prompting produced content-dependent spatial signals but was not reliable alone. Classical matching was most accurate when cross-modal structure was preserved but failed in the low-correspondence collection. A proposal-and-verify workflow used repeated VLM predictions as candidates and image-based similarity to select the final location. This workflow recovered useful localization in the low-correspondence regime.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.18309 [cs.CV]
(or arXiv:2608.18309v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.18309
arXiv-issued DOI via DataCite (pending registration)
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From: Xiangyu Yin [view email] [v1] Tue, 18 Aug 2026 20:41:07 UTC (11,506 KB)
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