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Application of Spectral Estimation Techniques to the Improvement of a 3D-color Digitizing Camera

2004· article· en· W2397814520 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConference on Colour in Graphics Imaging and Vision · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMultispectral imageWavelengthOpticsColor differenceArtificial intelligenceComputer scienceDigital cameraLaserLaser scanningColorimetryComputer visionMaterials scienceMathematicsComputer graphics (images)Physics

Abstract

fetched live from OpenAlex

This paper reports on the colorimetric improvement of a multispectral 3D digitizer through scanning at optimized wavelengths. These wavelengths were first established theoretically based on the criteria of minimal CIEDE2000 color difference over the set of reflectance curves from the full OSA-UCS catalog. A PCA-based and a spline-based spectral estimation method were considered, and sets of three, four and five optimal sampling wavelengths were derived for each method. This provided a basis for the selection of HeCd, ArKr, HeNe and DPSS commercial laser lines for which the colorimetric performance was predicted. This was then tested in the lab, where colour rendition charts were scanned with the camera at seven wavelengths, after which the charts were computer-rendered on a CRT display. Both the theoretical prediction and the experimental observation indicate that four well-chosen wavelengths are adequate for proper rendition of the charts.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.312
Teacher spread0.301 · 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