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Record W2109242101 · doi:10.1109/cvpr.1988.196288

Color constancy computation in near-Mondrian scenes using a finite dimensional linear model

2003· article· en· W2109242101 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

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMondrianColor constancyLightnessComputationComputer visionArtificial intelligenceComputer scienceFrench hornComputer graphics (images)AlgorithmImage (mathematics)PaintingPhysicsAcoustics

Abstract

fetched live from OpenAlex

The authors combine an alternate version of B.K.P. Horn's computation (1974) for recovering lightness from Land's retinex scheme, as modified by A. Blake (1985), with a finite-dimensional linear model for color images. The authors recover surface spectral reflectance from images of near-Mondrian scenes by using edge detection and derivatives to screen out slowly changing illumination from color scene images. They first develop a model for lightness recovery, based on Horn's, because a similar treatment is needed for the FDM case (a particular finite-dimensional linear model). Based on the equations for the one-dimensional case, they develop a model of surface reflectance recovery for near-Mondrian images by extending the basic approach to the vector model of illumination, reflectance, and receptors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.244

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 head0.032
GPT teacher head0.303
Teacher spread0.271 · 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

Quick stats

Citations74
Published2003
Admission routes1
Has abstractyes

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