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Record W3117524551 · doi:10.21611/qirt.2020.151

Application of Blind Image Quality Assessment Metrics To Pulsed Thermography

2020· article· en· W3117524551 on OpenAlexafffund
Julien Fleuret, Samira Ebrahimi, Clemente Ibarra‐Castanedo, Xavier Maldague

Bibliographic record

VenueProceedings of the 2020 International Conference on Quantitative InfraRed Thermography · 2020
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversité Laval
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsThermographyImage qualityQuality (philosophy)Quality assessmentComputer scienceImage (mathematics)Artificial intelligenceComputer visionMaterials scienceOpticsReliability engineeringEngineeringEvaluation methodsPhysicsInfrared

Abstract

fetched live from OpenAlex

This paper introduces an application of Blind Image Quality Assessment (BIQA) metrics to pulsed thermography. This study investigates the application of BIQA in association with state of the art methods in order to improve the detection of defects in PT sequences. The experiments show that BIQA may significantly improve the detection of defects when applied to steel, but fails to capture informative features on CFRP.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.327
Teacher spread0.274 · 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 teacher head, 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

Citations1
Published2020
Admission routes2
Has abstractyes

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