NEXT‑MET

Next‑Generation Optical Metrology: Machine Learning Strategies for Complex Measurements

A proposed Special Session at the XXV IMEKO World Congress 2027, bringing together researchers working at the intersection of optical metrology — camera, confocal, interferometric and tomographic sensing — and machine learning for denoising, reconstruction, and trustworthy measurement.

Rimini, Italy 30 Aug – 3 Sep 2027 Host: XXV IMEKO World Congress TC2 · TC6 · TC7 · TC11 · TC14 · TC21
View topics of interest Submission guidelines
Scope

The rapid evolution of optical metrology—including camera-based systems, confocal sensing, interferometry, and tomography—has significantly improved the precision, flexibility, and applicability of modern measurement systems, establishing it as a key tool for industrial inspection and experimental analysis across manufacturing, biomedical imaging, and scientific research.

Real-world measurement scenarios introduce substantial challenges: photon shot noise, electronic interference, environmental disturbance, missing or corrupted data from occlusions and hardware limits — compounded by the need for real-time, low-latency processing at scale. Traditional signal processing and calibration methods require extensive manual tuning and generalize poorly across setups.

Recent advances in machine learning and deep learning offer promising solutions — denoising, reconstruction, defect detection, high-dimensional data analysis — yet open challenges remain in labeled-data scarcity, cross-instrument robustness, interpretability, and validation against physical standards. NEXT‑MET brings these two communities together.

PHYSICS-INFORMED AI MEASUREMENT UNCERTAINTY METROLOGICAL TRACEABILITY DATA RECONSTRUCTION EXPLAINABLE AI PRECISION METROLOGY

Target technical committees

  • TC2Photonics
  • TC6Digitalization
  • TC7Measurement Science
  • TC11Testing, Inspection & Certification
  • TC14Measurement of Geometrical Quantities
  • TC21Mathematical Tools
Call for papers

Topics of interest

We specifically encourage contributions addressing the following cutting-edge areas, including — but not limited to:

Optical Signal Reconstruction & Enhancement

High-resolution reconstruction from raw optical signals; super-resolution, phase retrieval, depth reconstruction; denoising and artifact removal; handling missing data and occlusions.

Synthetic Data Generation for Metrology

Physics-based simulation for training/validating ML models; GANs and diffusion models for realistic optical data; synthetic rare events and sensor failures; domain adaptation; reproducibility ethics.

Benchmarking, Validation & Standardisation

Validation frameworks aligned with traceability and uncertainty standards; benchmarks for reconstruction fidelity and robustness; cross-instrument generalization; open dataset creation.

Dynamic & Temporal Measurement Contexts

ML/DL for temporal optical data streams; drift and evolving noise patterns; micro-batch and streaming pipelines for real-time measurement and reconstruction.

Learning Frameworks for Optical Metrology

Supervised, unsupervised and self-supervised learning; physics-informed neural networks; uncertainty quantification; continual and lifelong learning.

Anomaly, Novelty & Drift Detection

Detecting anomalies and defects in optical measurements; concept-drift monitoring (sensor aging, calibration drift); synthetic anomalies for testing detection frameworks.

Applications in Industry, Biomedical & Science

High-precision manufacturing; biomedical imaging (confocal, OCT, tomography); scientific instrumentation; industrial quality control and defect characterization.

Schedule

Important dates

Following the official IMEKO World Congress 2027 schedule; applies to papers submitted to the NEXT‑MET special session.

15 Sep 2026
Special session proposal deadline (NEXT‑MET → IMEKO 2027)
30 Sep 2026
Special session acceptance notification
15 Oct 2026
Extended abstract submission opens
15 Jan 2027
Extended abstract submission deadline · minimum 4 pages
28 Feb 2027
Notification of abstract acceptance
3 – 18 May 2027
Revised full paper window · max. 6 pages
25 May – 15 Jun 2027
Final full paper window · max. 6 pages
30 Aug – 3 Sep 2027
XXV IMEKO World Congress · Rimini, Italy
Authors

Submission guidelines

  1. Prepare an Extended Abstract (4–5 pages) addressing one or more Topics of Interest above, following the official IMEKO 2027 Word or LaTeX template (congress site → Authors → Initial Author Instructions).

  2. Submit via EDAS, the official congress platform: edas.info/newPaper.php?c=35447 — select NEXT‑MET as the target special session.

  3. Each submission is reviewed by at least two independent reviewers on relevance, technical quality, scientific merit and originality — the same process as regular congress papers.

  4. Accepted authors revise into a Final Paper (max. 6 pages) addressing reviewer feedback, within the congress-wide windows above.

Publication: accepted papers appear in the IMEKO World Congress 2027 proceedings in open-access format; a selection will be recommended for extended versions in peer-reviewed journals.
Organizers

Session organizers

Marco Piangerelli

Marco Piangerelli

University of Camerino · Vici & C. S.p.A., Italy

He works on unsupervised machine learning and data science for manufacturing, metrology, and bio-science, with a focus on self-adaptive systems and topological data analysis. He co-organizes international workshops on synthetic data and streaming learning at ECML-PKDD and KDD.

marco.piangerelli@vici.it
marco.piangerelli@unicam.it
Ruidong Xue

Ruidong Xue

University of Nottingham, United Kingdom

He works on machine learning for optical metrology, developing digital twins, virtual instrument simulations, and ML pipelines for signal processing and 3D surface reconstruction. He is a Research Fellow on a multi-million-pound ERC-funded project on AI-enhanced surface metrology.

ruidong.xue@nottingham.ac.uk
Filippo Zanini

Filippo Zanini

University of Padova, Italy

He works on dimensional metrology and precision manufacturing, with a focus on industrial X-ray computed tomography and in-line/in-situ inspection. He applies machine learning to improve high-speed tomography accuracy and automate defect detection in additive manufacturing.

filippo.zanini@unipd.it
Invited speaker

To be revealed

We’re finalizing our invited speaker — announcement planned once the special session is confirmed and the abstract submission window closes.

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Reviewers

Program committee

Marco PiangerelliUniversity of Camerino, School of Science and Technology (Computer Science Division) / Vici & C. S.p.A., Italy
Ruidong XueUniversity of Nottingham, Department of Mechanical, Materials and Manufacturing Engineering, United Kingdom
Filippo ZaniniUniversity of Padova, Department of Management and Engineering, Italy
Nicolò BonatoUniversity of Padova, Department of Management and Engineering, Italy
Matteo CalaonTechnical University of Denmark, Department of Civil and Mechanical Engineering
Gaoliang DaiPhysikalisch-Technische Bundesanstalt (PTB), Department 5.2 – Dimensional Nanometrology, Germany
Samanta PianoUniversity of Nottingham, Faculty of Engineering, United Kingdom
Sofia CatalucciUniversity of Padova, Department of Industrial Engineering, Italy
Giacomo MaculottiPolitecnico di Torino, Department of Management and Production Engineering (DIGEP), Italy
TBCReviewer confirming
TBCReviewer confirming

Venue

Palacongressi di Rimini
Via della Fiera, 23 · 47923 Rimini, Italy

riminipalacongressi.it →

Contact

Questions about NEXT‑MET scope & fit: contact the organizers above.

General IMEKO 2027 congress questions: info@imeko2027.org

Official session listing on imeko2027.org →

imeko2027.org →