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Record W2045401014 · doi:10.1117/12.776781

A straightforward graphical user interface for basic and advanced signal processing of thermographic infrared sequences

2008· article· en· W2045401014 on OpenAlexaffabout
M. Klein, Clemente Ibarra‐Castanedo, Xavier Maldague, Abdelhakim Bendada

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThermographyGraphical user interfaceComputer scienceMATLABSoftwareLicenseInterface (matter)InfraredField (mathematics)Human–computer interactionSoftware engineeringOpen sourceSignal processingComputer graphics (images)Engineering drawingComputer hardwareOperating systemEngineeringDigital signal processingOptics

Abstract

fetched live from OpenAlex

IR-View, is a free and open source Matlab software that was released in 1998 at the Computer Vision and Systems Laboratory (CVSL) at Université Laval, Canada, as an answer to many common and recurrent needs in Infrared thermography. IR-View has proven to be a useful tool at CVSL for the past 10 years. The software by itself and/or its concept and functions may be of interest for other laboratories and companies working in research in the IR NDT field. This article describes the functions and processing techniques integrated to IR-View, freely downloadable under the GNU license at http://mivim.gel.ulaval.ca. Demonstration of IR-View functionalities will also be done during the DSS08 SPIE Defense and Security Symposium.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.276
Threshold uncertainty score0.923

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2760.097

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations27
Published2008
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicThermography and Photoacoustic TechniquesFrench-language works237,207