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Record W1981137939 · doi:10.1002/col.20680

The Logvinenko object color atlas in practice

2011· article· en· W1981137939 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueColor Research & Application · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAtlas (anatomy)Artificial intelligenceColor spaceComputer scienceComputer visionColor modelObject (grammar)Color differencePattern recognition (psychology)Computer graphics (images)Image (mathematics)

Abstract

fetched live from OpenAlex

Abstract Recently, Logvinenko introduced a new object‐color space defining a complete object‐color atlas that is invariant to illumination. 1 However, the existing implementation for calculating the new atlas's color descriptors is computationally expensive and does not work for all types of illuminants. A new algorithm is presented here that efficiently calculates the required color descriptors over large data sets and across a wide variety of illuminants. Its Matlab implementation has been made available online. The algorithm is then used to explore some features and possible applications of Logvinenko's color atlas. In particular, it is applied to images to investigate the perceptual correlates of the color descriptors; it is used to predict how images change under a change of scene illumination; and it is used to evaluate how changes in illumination and sensor sensitivities affect the mapping from the Munsell to NCS color atlases. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011;

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.075
GPT teacher head0.407
Teacher spread0.333 · 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