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Record W3005148842 · doi:10.1364/ol.378939

Real-time high-speed three-dimensional surface imaging using band-limited illumination profilometry with a CoaXPress interface

2020· article· en· W3005148842 on OpenAlexafffund
Cheng Jiang, Patrick Kilcullen, Xianglei Liu, Jeremy Gribben, Alan R. Boate, T. Ozaki, Jinyang Liang

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsProfilometerOpticsInterface (matter)Projection (relational algebra)Computer scienceDigital Light ProcessingTransmission (telecommunications)Data acquisitionPhysicsSurface roughness

Abstract

fetched live from OpenAlex

High-speed three-dimensional (3D) surface imaging by structured-light profilometry is currently driven by numerous applications. However, the limited speeds in fringe pattern projection, image acquisition, and data transmission have strained the existing methods from reaching kilohertz-level acquisition, processing, and display of 3D information during the occurrence of dynamic events (i.e., in real time). To overcome these limitations, we have developed band-limited illumination profilometry (BLIP) with a CoaXPress interface (CI), which enables real-time high-speed 3D surface imaging. We have demonstrated the system's performance by imaging various static and fast-moving 3D objects in real time. We have also applied this system in fluid mechanics by imaging dynamics of a flag, which allowed observation of the wave propagation, gravity-induced phase mismatch, and asymmetric flapping motion. We expect CI-BLIP to find diverse scientific and industrial applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.249
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2020
Admission routes2
Has abstractyes

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